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High performance feature selection algorithms using filter method for cloud-based recommendation system

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Abstract

In cloud-based recommendation system, the feature selection is implemented to reduce the large dimension of the cloud data. The feature selection increases the performance of the recommendation system without affecting the accuracy of the system. In this paper two filter model based algorithms SFS and MSFS are proposed to extract the necessary features for the recommendation system. The state of the art Naive bayes classification algorithm is used to evaluate the performance of the feature selection algorithm. The bench mark datasets Newsgroups, WebKB and Book Crossing are used for performance evaluation. The experimental results show that the proposed algorithm is superior to the existing feature selection algorithms T-Score, Information Gain and Chi squared.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, KSR Kalvi Nagar, Tiruchengode, Namakkal, Tamil Nadu, 637215, India

    D. Muthusankar & P. Kaladevi

  2. Department of Computer Science and Engineering, K.S.R. Institute for Engineering and Technology, KSR Kalvi Nagar, Tiruchengode, Namakkal, Tamil Nadu, 637215, India

    B. Kalaavathi

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  1. D. Muthusankar
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  2. B. Kalaavathi
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  3. P. Kaladevi
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Correspondence to D. Muthusankar.

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Muthusankar, D., Kalaavathi, B. & Kaladevi, P. High performance feature selection algorithms using filter method for cloud-based recommendation system. Cluster Comput 22 (Suppl 1), 311–322 (2019). https://doi.org/10.1007/s10586-018-1901-0

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