Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Feature selection is a very crucial step in data mining process. It aims to find the most important feature subset from a given feature set without degradation of classifying information. As for the traditional feature selection method, the number of candidate feature subsets created by algorithm in an iterative computational way is exponential in the size of the initial attribute set. And relevant algorithm occupies a lot of the system resources in time and space. In this paper, we study and develop a novel feature selection method and provide its mathematic principle, which is based on the factors of attributes contributing to target attribute and their maximum information divergence value (MIDV) to select small enough feature subset and improve the classification accuracy. And then the extensive experiment shows that our proposed method is very efficient in computational performance and scalability than traditional methods.