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Full-Text Articles in Social and Behavioral Sciences
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Faculty of Engineering and Information Sciences - Papers: Part A
Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …
Attention-Based High-Order Feature Interactions To Enhance The Recommender System For Web-Based Knowledge-Sharing Servic, Jiayin Lin, Geng Sun, Jun Shen, Tingru Cui, David Pritchard, Dongming Xu, Li Li, Wei Wei, Ghassan Beydoun, Shiping Chen
Attention-Based High-Order Feature Interactions To Enhance The Recommender System For Web-Based Knowledge-Sharing Servic, Jiayin Lin, Geng Sun, Jun Shen, Tingru Cui, David Pritchard, Dongming Xu, Li Li, Wei Wei, Ghassan Beydoun, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender …
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Faculty of Engineering and Information Sciences - Papers: Part B
In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic …
Application Of The Largest Lyapunov Exponent Algorithm For Feature Extraction In Low Speed Slew Bearing Condition Monitoring, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Application Of The Largest Lyapunov Exponent Algorithm For Feature Extraction In Low Speed Slew Bearing Condition Monitoring, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration …
Quality Of Experience-Based Image Feature Selection For Mobile Augmented Reality Applications, Yi Cao, Christian H. Ritz, Raad Raad
Quality Of Experience-Based Image Feature Selection For Mobile Augmented Reality Applications, Yi Cao, Christian H. Ritz, Raad Raad
Faculty of Engineering and Information Sciences - Papers: Part A
Mobile augmented reality applications rely on automatically recognising a visual scene through matching of derived image features. To ensure the Quality of Experience (QoE) perceived by users, such applications should achieve high matching accuracy meanwhile minimizing the waiting time to meet real-time requirement. An efficient solution is to develop an effective feature selection method to select the most robust features against distortions caused by camera capture to achieve high matching accuracy whilst transmission and matching process of the features are significant reduced. Feature selection is also beneficial to reducing the computational complexities of the matching system so that waiting time …
Adaptive And Robust Feature Selection For Low Bitrate Mobile Augmented Reality Applications, Yi Cao, Christian H. Ritz, Raad Raad
Adaptive And Robust Feature Selection For Low Bitrate Mobile Augmented Reality Applications, Yi Cao, Christian H. Ritz, Raad Raad
Faculty of Engineering and Information Sciences - Papers: Part A
Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features for the captured image, transmitting them through a network and then matching with features derived for a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimising the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image …
An Approach For Assessing The Effectiveness Of Multiple-Feature-Based Svm Method For Islanding Detection Of Distributed Generation, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
An Approach For Assessing The Effectiveness Of Multiple-Feature-Based Svm Method For Islanding Detection Of Distributed Generation, Mollah R. Alam, Kashem M. Muttaqi, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
Islanding detection is a critical protection issue, as conventional protection schemes such as vector surge (VS) and rate of change of frequency relays do not guarantee islanding detection for all network conditions. Integration of multiple distributed generation (DG) units of different sizes and technologies into distribution grids makes this issue even more critical. This paper presents a comprehensive analysis of the effectiveness of a new method for islanding detection in DG networks. The proposed method, which is based on multiple features and support vector machine (SVM) classification, has the potential to overcome the limitations of conventional protection schemes. The multifeature-based …
A Hybrid Feature Extraction Technique For Face Recognition, Qasim Alshebani, Prashan Premarante, Peter James Vial
A Hybrid Feature Extraction Technique For Face Recognition, Qasim Alshebani, Prashan Premarante, Peter James Vial
Faculty of Engineering and Information Sciences - Papers: Part A
The accuracy of any face recognition is important for many military and civilian real time applications. Based on current literature it has been proven that, the accuracy of a face recognition system can be extremely improved using a hybrid feature extraction technique. This paper presents a hybrid feature extraction technique to obtain high level of recognition accuracy. The facial topographical features are extracted using manual segmentation of facial regions of eyes, nose and mouth. The Gabor transform of the maximum of these regions are then extracted to calculate the local representations of these regions. In the classification stage, the Nearest …
Progressive Mode-Seeking On Graphs For Sparse Feature Matching, Chao Wang, Lei Wang, Lingqiao Liu
Progressive Mode-Seeking On Graphs For Sparse Feature Matching, Chao Wang, Lei Wang, Lingqiao Liu
Faculty of Engineering and Information Sciences - Papers: Part A
Sparse feature matching poses three challenges to graph-based methods: (1) the combinatorial nature makes the number of possible matches huge; (2) most possible matches might be outliers; (3) high computational complexity is often incurred. In this paper, to resolve these issues, we propose a simple, yet surprisingly effective approach to explore the huge matching space in order to significantly boost true matches while avoiding outliers. The key idea is to perform mode-seeking on graphs progressively based on our proposed guided graph density. We further design a density-aware sampling technique to considerably accelerate mode-seeking. Experimental study on various benchmark data sets …
An Application Of Nonlinear Feature Extraction – A Case Study For Low Speed Slewing Bearing Condition Monitoring And Prognosis, Wahyu Caesarendra, Prabuono Buyung Kosasih, A K. Tieu, Craig Moodie
An Application Of Nonlinear Feature Extraction – A Case Study For Low Speed Slewing Bearing Condition Monitoring And Prognosis, Wahyu Caesarendra, Prabuono Buyung Kosasih, A K. Tieu, Craig Moodie
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents the application of four nonlinear methods of feature extraction in slewing bearing condition monitoring and prognosis: these are largest Lyapunov exponent, fractal dimension, correlation dimension, and approximate entropy methods. Although correlation dimension and approximate entropy methods have been used previously, the largest Lyapunov exponent and fractal dimension methods have not been used in vibration condition monitoring to date. The vibration data of the laboratory slewing bearing test-rig run at 1 rpm was acquired daily from February to August 2007 (138 days). As time progressed, a more accurate observation of the alteration of bearing condition from normal to …
A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang
A Scalable Unsupervised Feature Merging Approach To Efficient Dimensionality Reduction Of High-Dimensional Visual Data, Lingqiao Liu, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrix-based linear projection causes significant computation and memory use. When the number of visual data is large, how to efficiently learn such a projection could even become a problem. The recent feature merging approach offers an efficient way to reduce the dimensionality, which only requires a single scan of features to perform reduction. However, existing merging algorithms do not scale well with high-dimensional data, especially in the unsupervised case. To …
Condition Monitoring Of Slow Speed Slewing Bearing Based On Largest Lyapunov Exponent Algorithm And Circular-Domain Feature Extractions, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Condition Monitoring Of Slow Speed Slewing Bearing Based On Largest Lyapunov Exponent Algorithm And Circular-Domain Feature Extractions, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie
Faculty of Engineering and Information Sciences - Papers: Part A
This paper presents a combined nonlinear and circular features extraction-based condition monitoring method for low speed slewing bearing. The proposed method employs the largest Lyapunov exponent (LLE) algorithm as a signal processing method based on vibration data. LLE is used to detect chaos existence in vibration data in discrete angular positions of the shaft. From the processed data, circular features such as mean, skewness and kurtosis are calculated and monitored. It is shown that the onset and the progressively deteriorating bearing condition can be detected more clearly in circular-domain features compared to time-domain features. The application of the method is …
A Reduced Reference Image Quality Metric Based On Feature Fusion And Neural Networks, Aladine Chetouani, Azeddine Beghdadi, Mohamed Deriche, Abdesselam Bouzerdoum
A Reduced Reference Image Quality Metric Based On Feature Fusion And Neural Networks, Aladine Chetouani, Azeddine Beghdadi, Mohamed Deriche, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
A Global Reduced Reference Image Quality Metric (IQM) based on feature fusion using neural networks is proposed. The main idea is the introduction of a Reduced Reference degradation-dependent IQM (RRIQM/D) across a set of common distortions. The first stage consists of extracting a set of features from the wavelet-based edge map. Such features are then used to identify the type of degradation using Linear Discriminant Analysis (LDA). The second stage consists of fusing the extracted features into a single measure using Artificial Neural Networks (ANN). The result is a degradation- dependent IQM measure called the RRIQM/D. The performance of the …
Efficient Spectral Feature Selection With Minimum Redundancy, Zheng Zhao, Lei Wang, Huan Liu
Efficient Spectral Feature Selection With Minimum Redundancy, Zheng Zhao, Lei Wang, Huan Liu
Faculty of Engineering and Information Sciences - Papers: Part A
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. …
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Faculty of Engineering and Information Sciences - Papers: Part A
Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate …
Feature Subset Selection For Multi-Class Svm Based Image Classification, Lei Wang
Feature Subset Selection For Multi-Class Svm Based Image Classification, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
Multi-class image classification can benefit much from feature subset selection. This paper extends an error bound of binary SVMs to a feature subset selection criterion for the multi-class SVMs. By minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. This minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of features are involved. Also, considering that image classification is often a small sample problem, the regularization issue is investigated for this criterion, showing its robustness in this situation. Experimental study on multiple benchmark image …