Conditional Probability Based Feature Selector for Effective Data Classification

2016 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
Features play a very important role in the task of pattern classification. Consequently, the selection of suitable features is necessary as most of the raw data might be redundant or irrelevant to the recognition of patterns. In some cases, the classifier cannot perform well because of the large number of redundant features. This work presents a novel evolving feature selection algorithms taking the advantages of conditional dependency to improve the predictive accuracy. Bayes Conditional
more » ... s Conditional dependency approach is used to discover dependency information among features. Different features play different roles in classifying datasets. Unwanted features will result in error information during classification which will reduce classification precision. The proposed feature selection can remove these distractions to improve classification performance. As shown in the experimental results, after feature selection using the proposed method to control false discovery rate, the classification performance of Decision Tree and Naïve Bayesian classifiers were significantly improved.
doi:10.21311/001.39.7.01 fatcat:wh5hvdjftzatxi56n7mryijzfe