Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Computer Engineering
Stability And Classification Performance Of Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar, Qianhui Liang
Stability And Classification Performance Of Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar, Qianhui Liang
Computer Science Faculty Publications
Feature selection techniques can be evaluated based on either model performance or the stability (robustness) of the technique. The ideal situation is to choose a feature selec- tion technique that is robust to change, while also ensuring that models built with the selected features perform well. One domain where feature selection is especially important is software defect prediction, where large numbers of met- rics collected from previous software projects are used to help engineers focus their efforts on the most faulty mod- ules. This study presents a comprehensive empirical ex- amination of seven filter-based feature ranking techniques (rankers) applied to …
Measuring Stability Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar
Measuring Stability Of Threshold-Based Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar
Computer Science Faculty Publications
Feature selection has been applied in many domains, such as text mining and software engineering. Ideally a feature selection technique should produce consistent out- puts regardless of minor variations in the input data. Re- searchers have recently begun to examine the stability (robustness) of feature selection techniques. The stability of a feature selection method is defined as the degree of agreement between its outputs to randomly-selected subsets of the same input data. This study evaluated the stability of 11 threshold-based feature ranking techniques (rankers) when applied to 16 real-world software measurement datasets of different sizes. Experimental results demonstrate that AUC …
Measuring Robustness Of Feature Selection Techniques On Software Engineering Datasets, Huanjing Wang, Taghi Khoshgoftaar, Randall Wald
Measuring Robustness Of Feature Selection Techniques On Software Engineering Datasets, Huanjing Wang, Taghi Khoshgoftaar, Randall Wald
Computer Science Faculty Publications
Feature Selection is a process which identifies irrelevant and redundant features from a high-dimensional dataset (that is, a dataset with many features), and removes these before further analysis is performed. Recently, the robustness (e.g., stability) of feature selection techniques has been studied, to examine the sensitivity of these techniques to changes in their input data. In this study, we investigate the robustness of six commonly used feature selection techniques as the magnitude of change to the datasets and the size of the selected feature subsets are varied. All experiments were conducted on 16 datasets from three real-world software projects. The …
Vehicular Ad Hoc Networks, Syed R. Rizvi, Stephan Olariu, Christina M. Oinotti, Shaharuddin Salleh, Mona E. Rizvi, Zainab Zaidi
Vehicular Ad Hoc Networks, Syed R. Rizvi, Stephan Olariu, Christina M. Oinotti, Shaharuddin Salleh, Mona E. Rizvi, Zainab Zaidi
Computer Science Faculty Publications
(First paragraph) Vehicular ad hoc networks (VANETs) have recently been proposed as one of the promising ad hoc networking techniques that can provide both drivers and passengers with a safe and enjoyable driving experience. VANETs can be used for many applications with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In the United States, motor vehicle traffic crashes are the leading cause of death for all motorists between two and thirty-four years of age. In 2009, the National Highway Traffic Safety Administration (NHTSA) reported that 33,808 people were killed in motor vehicle traffic crashes. The US Department of Transportation (US-DOT) estimates that …