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Artificial Intelligence and Robotics Commons™
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- Data Mining, Software Engineering (4)
- Classification (2)
- Defect Prediction (2)
- Feature Selection (2)
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- Software Metrics (2)
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- Threshold-based feature selection technique (2)
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Articles 1 - 12 of 12
Full-Text Articles in Artificial Intelligence and Robotics
A Fair Assignment Algorithm For Multiple Preference Queries, Leong Hou U, Nikos Mamoulis, Kyriakos Mouratidis
A Fair Assignment Algorithm For Multiple Preference Queries, Leong Hou U, Nikos Mamoulis, Kyriakos Mouratidis
Kyriakos MOURATIDIS
Consider an internship assignment system, where at the end of each academic year, interested university students search and apply for available positions, based on their preferences (e.g., nature of the job, salary, office location, etc). In a variety of facility, task or position assignment contexts, users have personal preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple simultaneous requests, a single object cannot be assigned to more than one users. The challenge is …
Medoid Queries In Large Spatial Databases, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou
Medoid Queries In Large Spatial Databases, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou
Kyriakos MOURATIDIS
Assume that a franchise plans to open k branches in a city, so that the average distance from each residential block to the closest branch is minimized. This is an instance of the k-medoids problem, where residential blocks constitute the input dataset and the k branch locations correspond to the medoids. Since the problem is NP-hard, research has focused on approximate solutions. Despite an avalanche of methods for small and moderate size datasets, currently there exists no technique applicable to very large databases. In this paper, we provide efficient algorithms that utilize an existing data-partition index to achieve low CPU …
Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis
Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis
Kyriakos MOURATIDIS
Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as °uctuations of edge weights. The ¯rst one maintains the query results by processing only updates that may invalidate …
Expert System For Online Diagnosis Of Red-Eye Diseases, Dr. Muhammad Zubair Asghar, Muhammad Junaid Asghar
Expert System For Online Diagnosis Of Red-Eye Diseases, Dr. Muhammad Zubair Asghar, Muhammad Junaid Asghar
Dr. Muhammad Zubair Asghar
This paper describes Expert System (ES) for online diagnosis and prescription of red-eye diseases. The types of eye diseases that can be diagnosed with this system are called Red-eye diseases i.e. disease in which red-eye is the common symptom. It is rule based web-supported expert system, assisting ophthalmologists, medical students doing specialization in ophthalmology, researchers as well as eye patients having computer know-how. System was designed and programmed with Java Technology. The expert rules were developed on the symptoms of each type of Red-eye disease, and they were presented using tree-graph and inferred using forward-chaining with depth-first search method. User …
A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse
A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse
Dr. Huanjing Wang
Abstract Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The …
A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
Dr. Huanjing Wang
One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can …
A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse
A Comparative Study Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse
Computer Science Faculty Publications
Abstract Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The …
A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
Computer Science Faculty Publications
One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can …
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum …
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Research Collection School Of Computing and Information Systems
Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) …
Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany
Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany
Conference papers
In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation- based techniques in parallel with techniques such as cross-validation ac- curacy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity mea- sures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing …
Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany
Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany
Conference papers
Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.