Performance Evaluation of feature selection methods for Mobile devices

Abstract

Machine Learning deals with programming computers that learn from experience. The field of Machine learning is a popular research area in Computer Science. These techniques are helpful in different fields of Computer Science, Mobile Computing, Bioinformatics, Digital Forensic, Agriculture and Text Classification. Machine learning classification algorithms are used in Pattern Recognition, Text Categorization, Mobile message classification, Mobile Image tagging applications, Mobile music interaction, Mobile learning. An optimized Naïve Bayes classifier is used for this work .In this work performance evaluation of three feature selection methods with optimized Naïve Bayes is performed on mobile device. Correlation based method, Gain Ratio method and Information Gain method methods were used in this work. The three methods are evaluated for performance measures of Accuracy, Precision, True Positive Rate, F- Measure, Recall, Mathew‟s Correlation Coefficient and Receiver Operating Characteristics.