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Computer Sciences Commons

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Computer Science Faculty Publications

2011

Classification

Articles 1 - 2 of 2

Full-Text Articles in Computer Sciences

Stability And Classification Performance Of Feature Selection Techniques, Huanjing Wang, Taghi Khoshgoftaar, Qianhui Liang Dec 2011

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 …


Centinela: A Human Activity Recognition System Based On Acceleration And Vital Sign Data, Óscar D. Lara, Alfredo J. Perez, Miguel A. Labrador, José D. Posada Jul 2011

Centinela: A Human Activity Recognition System Based On Acceleration And Vital Sign Data, Óscar D. Lara, Alfredo J. Perez, Miguel A. Labrador, José D. Posada

Computer Science Faculty Publications

This paper presents Centinela, a system that combines acceleration data with vital signs to achieve highly accurate activity recognition. Centinela recognizes five activities: walking, running, sitting, ascending, and descending. The system includes a portable and unobtrusive real-time data collection platform, which only requires a single sensing device and a mobile phone. To extract features, both statistical and structural detectors are applied, and two new features are proposed to discriminate among activities during periods of vital sign stabilization. After evaluating eight different classifiers and three different time window sizes, our results show that Centinela achieves up to 95.7% overall accuracy, which …