Feature Weighting Method Based On Instance Correlation Using Discretization
نویسنده
چکیده
In Machine Learning Process, several issues arise in identifying a suitable and quality set of features from which a classification model for a particular domain to be constructed. This paper addresses the problem of feature selection for machine learning through discretization approach. RELIEF is considered to be one of the most successful algorithms for assessing the quality of features. RELIEF algorithm selects the near instance and far away instance and assigns weight to the selected instance by sampling method. Sampling method will deviate in selecting the relevant features. So, a new feature weighting method is proposed which gives high correlation between the instances and the main objective is to select features that have highly correlated instances with the class. Experimental analysis shows better performance of the new algorithm in comparison with the existing RELIEF algorithm. The data set is taken from UCI ML repository for experiment. Results show that the new method can be successfully used with classifiers.
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تاریخ انتشار 2016