Abstract
Feature selection is one of the significant areas of research in the field of data mining, pattern recognition, and machine learning. One of the effective methods of feature selection is to determine the distinctive capability of the individual feature. More the distinctive capability the more interesting the feature is. But in addition to this, another important thing to be considered is the dependency between the different features. Highly dependent features lead to the inaccurate analysis or results. To solve this problem, we present an approach for feature selection based on the correlation analysis (ASCA) between the features. The algorithm works by iteratively removing the features that is highly dependent on each other. Firstly, we define the concept of multi-collinearity and its application to feature selection. Then, we present a new method for selection of attributes based on the correlation analysis. Finally, the proposed approach is tested on the benchmark datasets and the experimental results show that this approach works better than other existing feature selection algorithms both in terms of accuracy and computational overheads.
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Bedi, J., Toshniwal, D. (2018). Attribute Selection Based on Correlation Analysis. In: Rajsingh, E., Veerasamy, J., Alavi, A., Peter, J. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-10-7200-0_5
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DOI: https://doi.org/10.1007/978-981-10-7200-0_5
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