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Adaptive Feature Selection and Classification Using Optimization Technique

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Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1013))

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Abstract

Today, the dimensions of the datasets are becoming more and more. From the existing or collected features, some are useful and some are not useful. In order to analyze the dataset, a feature subset can be selected by applying feature selection method. To select best features in lesser time, optimization algorithms like Particle Swarm Optimization (PSO) can be implemented. There is a possibility of redundant features getting selected based on the correlation among features. Pearson correlation coefficient is being employed to reduce the redundancy. Further, PSO is implemented for classification. Datasets analyzed in this research work are, namely, Australian credit, German credit, Iris, Thyroid, Vehicle, WBC, and Wine. It is observed that convincing results were obtained from the proposed method.

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Author information

Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Nekuri Naveen

  2. Computer Science and Engineering School of Technology, GITAM University, Hyderabad, India

    Mandala Sookshma

Authors
  1. Nekuri Naveen
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  2. Mandala Sookshma
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Corresponding author

Correspondence to Nekuri Naveen .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Duy Tan University, Da Nang, Vietnam

    Bao Le Nguyen

  4. Graduate School, Duy Tan University, Da Nang, Vietnam

    Nhu Gia Nguyen

  5. Faculty of Information Technology, Hai Phong University, Hai Phong, Vietnam

    Dac-Nhuong Le

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Naveen, N., Sookshma, M. (2020). Adaptive Feature Selection and Classification Using Optimization Technique. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-32-9186-7_17

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