biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...

Feature usability index is introduced here as a measure for evaluating classification efficacy of features. It is defined using measures of homogeneity, class specificity, and error in decision making. Homogeneity measures the extent of outlying observations, class specificity assesses the separation between distributions of different labeled classes, and error in decision making is computed us...

Real world classification applications usually have many features. This increases the complexity of the classification task. Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge. We present a new feature subset selection method by using sorted feature relevance. We tested the new method on real world and artificial datasets and compared its res...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...