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Research Collection School Of Computing and Information Systems

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Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng Dec 2014

Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng

Research Collection School Of Computing and Information Systems

With the rapid growth of the mobile market, security of mobile platforms is receiving increasing attention from both research community as well as the public. In this paper, we make the first attempt to establish a baseline for security comparison between the two most popular mobile platforms. We investigate applications that run on both Android and iOS and examine the difference in the usage of their security sensitive APIs (SS-APIs). Our analysis over 2,600 applications shows that iOS applications consistently access more SS-APIs than their counterparts on Android. The additional privileges gained on iOS are often associated with accessing private …

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High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi Dec 2014

High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order …

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From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak Dec 2014

From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Through their normal operation, cellular networks are a repository of continuous location information from their subscribed devices. Such information, however, comes at a coarse granularity both in terms of space, as well as time. For otherwise inactive devices, location information can be obtained at the granularity of the associated cellular sector, and at infrequent points in time, that are sensitive to the structure of the network itself, and the level of mobility of the device. In this paper, we are asking the question of whether such sparse information can help to identify the paths followed by mobile connected devices throughout …

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Player Acceptance Of Human Computation Games: An Aesthetic Perspective, Xiaohui Wang, Dion Hoe Lian Goh, Ee Peng Lim, Adrian Wei Liang Vu Nov 2014

Player Acceptance Of Human Computation Games: An Aesthetic Perspective, Xiaohui Wang, Dion Hoe Lian Goh, Ee Peng Lim, Adrian Wei Liang Vu

Research Collection School Of Computing and Information Systems

Human computation games (HCGs) are applications that use games to harness human intelligence to perform computations that cannot be effectively done by software systems alone. Despite their increasing popularity, insufficient research has been conducted to examine the predictors of player acceptance for HCGs. In particular, prior work underlined the important role of game enjoyment in predicting acceptance of entertainment technology without specifying its driving factors. This study views game enjoyment through a taxonomy of aesthetic experiences and examines the effect of aesthetic experience, usability and information quality on player acceptance of HCGs. Results showed that aesthetic experience and usability were …

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An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta Nov 2014

An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta

Research Collection School Of Computing and Information Systems

The maintenance of software products has been studied extensively in both software engineering and management information systems. Such studies are mainly focused on the activities that take place prior to starting the maintenance phase. Their contribution is either related to the improvement of software quality or to validating contingency models for reducing maintenance efforts. The continuous maintenance philosophy suggests to shift the attention within the maintenance phase for better coping with the evolutionary trajectories of digital platforms. In this paper, we examine the maintenance process of a digital platform from the perspective of the software vendor. Based on our empirical …

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Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta Nov 2014

Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta

Research Collection School Of Computing and Information Systems

There can be little contention about Stroustrup's epigrammatic remark: our civilization runs on software. However a caveat is increasingly due, much of the software that runs our civilization, runs on mobile devices today. Mobile operating systems have come to play a preeminent role in the ubiquity and utility of such devices. The development ecosystem of Android - one of the most popular mobile operating systems - presents an interesting context for studying whether and how collaboration dynamics in mobile development differ from conventional software development. In this paper, we examine factors that influence task ownership in Android development. Our results …

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Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni Nov 2014

Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni

Research Collection School Of Computing and Information Systems

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb …

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Exploiting Geographical Neighborhood Characteristics For Location Recommendation, Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao Nov 2014

Exploiting Geographical Neighborhood Characteristics For Location Recommendation, Yong Liu, Wei Wei, Aixin Sun, Chunyan Miao

Research Collection School Of Computing and Information Systems

Geographical characteristics derived from the historical check-in data have been reported effective in improving location recommendation accuracy. However, previous studies mainly exploit geographical characteristics from a user’s perspective, via modeling the geographical distribution of each individual user’s check-ins. In this paper, we are interested in exploiting geographical characteristics from a location perspective, by modeling the geographical neighborhood of a location. The neighborhood is modeled at two levels: the instance-level neighborhood defined by a few nearest neighbors of the location, and the region-level neighborhood for the geographical region where the location exists. We propose a novel recommendation approach, namely Instance-Region Neighborhood …

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Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi Nov 2014

Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize classification performance using minimal human labeling effort during the entire online stream data mining task. In this paper, we present a new family of algorithms for online active learning called Passive-Aggressive Active (PAA) learning algorithms by adapting the popular Passive-Aggressive algorithms in an online active …

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Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang Nov 2014

Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

We address the problem of visual instance mining, which is to extract frequently appearing visual instances automatically from a multimedia collection. We propose a scalable mining method by exploiting Thread of Features (ToF). Specifically, ToF, a compact representation that links consistent features across images, is extracted to reduce noises, discover patterns, and speed up processing. Various instances, especially small ones, can be discovered by exploiting correlated ToFs. Our approach is significantly more effective than other methods in mining small instances. At the same time, it is also more efficient by requiring much fewer hash tables. We compared with several state-of-the-art …

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Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo Nov 2014

Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts, which are often noisy or too few to accurately describe the image content. Second, ranking functions are trained on query-image pairs labeled by human labelers, making the annotation intellectually expensive and thus cannot be scaled up. We demonstrate that the above two fundamental challenges …

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Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi, Jialei Wang, Bin Li Nov 2014

Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi, Jialei Wang, Bin Li

Research Collection School Of Computing and Information Systems

This paper investigates a new machine learning framework of Online Transfer Learning (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general a challenging problem since data in both source and target domains not …

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Deep Learning For Content-Based Image Retrieval: A Comprehensive Study, Ji Wan, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, Jintao Li Nov 2014

Deep Learning For Content-Based Image Retrieval: A Comprehensive Study, Ji Wan, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, Jintao Li

Research Collection School Of Computing and Information Systems

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep …

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Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang Nov 2014

Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang

Research Collection School Of Computing and Information Systems

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two …

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Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen Nov 2014

Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with dd-dimension often lies in Euclidean space RdRd, whereas the covariance matrix typically resides on Riemannian manifold Sym+dSymd+. Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1Symd+1+. To fuse these statistics from heterogeneous spaces, we propose a Hybrid …

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Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu Nov 2014

Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu

Research Collection School Of Computing and Information Systems

Manual annotation of celebrities in Web videos is an essential task in many people-related Web services. The task, however, poses a significant challenge even to skillful annotators, mainly due to the large quantity of unfamiliar and greatly varied celebrities, and the lack of a customized system for it. This work develops CeleLabel, an interactive system for manually annotating celebrities in the Web video domain. The peculiarity of CeleLabel is to exploit and display multiple types of information that could assist the annotation, including video content, context surrounding and within a video, celebrity images on the Web, and human factors. Using …

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Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw Nov 2014

Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …

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Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis Nov 2014

Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and …

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Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw Nov 2014

Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set …

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The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao Nov 2014

The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao

Research Collection School Of Computing and Information Systems

The importance of multimedia travel guide search and recommender systems has led to a substantial amount of research spanning different computer science and information system disciplines in recent years. The five core research streams we identify here incorporate a few multimedia computing and information retrieval problems that relate to the alternative perspectives of algorithm design for optimizing search/recommendation quality and different methodological paradigms to assess system performance at large scale. They include (1) query analysis, (2) diversification based on different criteria, (3) ranking and reranking, (4) personalization and (5) evaluation. Based on a comprehensive discussion and analysis of these streams, …

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Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder Nov 2014

Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder

Research Collection School Of Computing and Information Systems

In large scale software development ecosystems, there is a common perception that higher developer involvement leads to faster resolution of bugs. This is based on conjectures around more ``eyeballs" making bugs "shallow" -- whose validity and applicability are not without dispute. In this paper, we posit that the level of developer attention as well as its extent of diversity influence how quickly bugs get resolved. We report results from a study of 1,000+ Android bugs. We find statistically significant evidence that attention and diversity have contrasting relationships with the resolution time of bugs, even after controlling for factors such as …

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On Joint Modeling Of Topical Communities And Personal Interest In Microblogs, Tuan-Anh Hoang, Ee Peng Lim Nov 2014

On Joint Modeling Of Topical Communities And Personal Interest In Microblogs, Tuan-Anh Hoang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In this paper, we propose the Topical Communities and Personal Interest (TCPI) model for simultaneously modeling topics, topical communities, and users’ topical interests in microblogging data. TCPI considers different topical communities while differentiating users’ personal topical interests from those of topical communities, and learning the dependence of each user on the affiliated communities to generate content. This makes TCPI different from existing models that either do not consider the existence of multiple topical communities, or do not differentiate between personal and community’s topical interests. Our experiments on two Twitter datasets show that TCPI can effectively mine the representative topics for …

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Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen Nov 2014

Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen

Research Collection School Of Computing and Information Systems

In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, …

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Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang Oct 2014

Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang

Research Collection School Of Computing and Information Systems

Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations1 on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. …

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A Multi-Dimensional Image Quality Prediction Model For User-Generated Images In Social Networks, You Yang, Xu Wang, Tao Guan, Jialie Shen, Li Yu Oct 2014

A Multi-Dimensional Image Quality Prediction Model For User-Generated Images In Social Networks, You Yang, Xu Wang, Tao Guan, Jialie Shen, Li Yu

Research Collection School Of Computing and Information Systems

User-generated images (UGIs) are currently proliferating within social networks. These images contain multi-dimensional data, including the image itself, text and the social links of the owner. UGIs can be utilized for self-presentation, news dissemination and other purposes, and the quality of the image should be able to reveal these social functionalities. However, it is challenging to predict UGI quality utilizing existing models, such as image quality assessment, recommender systems or others, because these models have difficulties processing multi-dimensional data simultaneously. To address this problem, we propose a multi-dimensional image quality prediction model for UGIs in social networks. In this model, …

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Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi Oct 2014

Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of …

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Time-Series Data Mining In Transportation: A Case Study On Singapore Public Train Commuter Travel Patterns, Roy Ka Wei Lee, Tin Seong Kam Oct 2014

Time-Series Data Mining In Transportation: A Case Study On Singapore Public Train Commuter Travel Patterns, Roy Ka Wei Lee, Tin Seong Kam

Research Collection School Of Computing and Information Systems

The adoption of smart cards technologies and automated data collection systems (ADCS) in transportation domain had provided public transport planners opportunities to amass a huge and continuously increasing amount of time-series data about the behaviors and travel patterns of commuters. However the explosive growth of temporal related databases has far outpaced the transport planners’ ability to interpret these data using conventional statistical techniques, creating an urgent need for new techniques to support the analyst in transforming the data into actionable information and knowledge. This research study thus explores and discusses the potential use of time-series data mining, a relatively new …

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Build Emotion Lexicon From Microblogs By Combining Effects Of Seed Words And Emoticons In A Heterogeneous Graph, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ling Chen, Chengqi Zhang Sep 2014

Build Emotion Lexicon From Microblogs By Combining Effects Of Seed Words And Emoticons In A Heterogeneous Graph, Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ling Chen, Chengqi Zhang

Research Collection School Of Computing and Information Systems

As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this paper, we propose a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method …

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The Use Of Geospatial Clustering In Analysing Health Risk Profile, Sue-Mae Yeo, Tin Seong Kam, Kai Xin Thia, Dan Wu Sep 2014

The Use Of Geospatial Clustering In Analysing Health Risk Profile, Sue-Mae Yeo, Tin Seong Kam, Kai Xin Thia, Dan Wu

Research Collection School Of Computing and Information Systems

Background & Hypothesis: The first law of geography states that “everything is related to everything else, but near things are more related than distant things”. This study aims to demonstrate how local indicator of spatial association (LISA) statistics are used to group patients with similar chronic diseases into natural clusters of hotspots found within northern Singapore by incorporating the proximity of their home locations explicitly. Methods: Anonymised chronic patient data collected from Khoo Teck Puat Hospital in 2013 were used for analyses. The data was mapped based on patients' residential addresses. A layer of hexagonal grid objects, each with a …

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Online Probabilistic Learning For Fuzzy Inference System, Richard Jayadi Oentaryo, Meng Joo Er, San Linn, Xiang Li Sep 2014

Online Probabilistic Learning For Fuzzy Inference System, Richard Jayadi Oentaryo, Meng Joo Er, San Linn, Xiang Li

Research Collection School Of Computing and Information Systems

Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended …

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