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Enhanced Prediction for Piezophilic Protein by Incorporating Reduced Set of Amino Acids Using Fuzzy-Rough Feature Selection Technique Followed by SMOTE

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Mathematics and Computing (ICMC 2018)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 253))

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Abstract

In this paper, the learning performance of different machine learning algorithms is investigated by applying fuzzy-rough feature selection (FRFS) technique on optimally balanced training and testing sets, consisting of the piezophilic and nonpiezophilic proteins. By experimenting using FRFS technique followed by Synthetic Minority Over-sampling Technique (SMOTE) at optimal balancing ratios, we obtain the best results by achieving sensitivity of 79.60%, specificity of 74.50%, average accuracy of 77.10%, AUC of 0.841, and MCC of 0.542 with random forest algorithm. The ranking of input features according to their differentiating ability of piezophilic and nonpiezophilic proteins is presented by using fuzzy-rough attribute evaluator. From the results, it is observed that the performance of classification algorithms can be improved by selecting the reduced optimally balanced training and testing sets. This can be obtained by selecting the relevant and non-redundant features from training sets using FRFS approach followed by suitably modifying the class distribution.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Institute of Science (BHU), Varanasi, India

    Anoop Kumar Tiwari & Karthikeyan Subbiah

  2. Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, India

    Shivam Shreevastava & Tanmoy Som

Authors
  1. Anoop Kumar Tiwari
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  2. Shivam Shreevastava
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  3. Karthikeyan Subbiah
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  4. Tanmoy Som
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Corresponding author

Correspondence to Shivam Shreevastava .

Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India

    Debdas Ghosh

  2. Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Debasis Giri

  3. Department of Mathematics, University of Central Florida, Orlando, FL, USA

    Ram N. Mohapatra

  4. Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Kouichi Sakurai

  5. UÅŸak University, UÅŸak, Turkey

    Ekrem Savas

  6. Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India

    Tanmoy Som

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Tiwari, A.K., Shreevastava, S., Subbiah, K., Som, T. (2018). Enhanced Prediction for Piezophilic Protein by Incorporating Reduced Set of Amino Acids Using Fuzzy-Rough Feature Selection Technique Followed by SMOTE. In: Ghosh, D., Giri, D., Mohapatra, R., Sakurai, K., Savas, E., Som, T. (eds) Mathematics and Computing. ICMC 2018. Springer Proceedings in Mathematics & Statistics, vol 253. Springer, Singapore. https://doi.org/10.1007/978-981-13-2095-8_15

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