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A comprehensive evaluation of Marine predator chaotic algorithm for feature selection of COVID-19

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Abstract

The COVID-19 disease has spread very swiftly in different parts of the world. Some of the implications of this disease are loss of life, health-related issues, negative impact on the economy, and several other social issues. For the purpose of forecasting this disease’s spread, various algorithms have been employed. Also, the application of several metaheuristic optimization algorithms has been explored for choosing optimal features from a big data set. This paper addresses this issue and proposes a chaotic algorithm based on Marine Predator Algorithm (MPA). A normalized fusion of chaotic function-is first proposed. The function is based on \(\beta\) chaotic map. Based on this function, position update mechanism is developed for improving the performance of the original MPA. The developed algorithm is named as Marine Predator Chaotic Algorithm (MPCA). The COVID-19 dataset has been employed for judging the efficacy of the proposed algorithms. Different statistical analyses and graphical visualizations affirm the efficacy of the proposed algorithms.

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Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

\(Y_{u} and Y_{l}\) :

Bounds of the variables

\(E_{m}\) :

Elite Matrix

Prey :

Prey Matrix

\(X^{TP}_{i,j}\) :

Member of Elite Matrix (\(i^{th}\) row and \(j^{th}\) coulumn)

\(X^{P}_{i,j}\) :

The location of \(i^{th}\) prey in \(j^{th}\) dimension.

\(R_{B}\) :

Random number generated with Brownian motion

\(R_{L}\) :

Random number generated with Levy motion

CF :

Bridging factor of Marine Predator Phase 2

\(\alpha _{i,j}\) :

Representation of search space of MPCA

\(\beta _{i,j}\) :

Discrete representation of search space of MPCA

\(R_{c}\) :

Cardinality of the dataset

\(\gamma _m(t)\) :

Binary Flag

Er(D):

Error in classification

\(R_{c}\) :

Cardinality of the dataset

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, India

    Akash Saxena

  2. School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466114, Madhya Pradesh, India

    Siddharth Singh Chouhan

  3. State Planning Institute, Lucknow, 226007, Uttar Pradesh, India

    Rabia Musheer Aziz

  4. Department of CSA, ITM University Gwalior, Gwalior, India

    Vani Agarwal

Authors
  1. Akash Saxena
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  2. Siddharth Singh Chouhan
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  3. Rabia Musheer Aziz
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Correspondence to Siddharth Singh Chouhan.

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Saxena, A., Chouhan, S.S., Aziz, R.M. et al. A comprehensive evaluation of Marine predator chaotic algorithm for feature selection of COVID-19. Evolving Systems 15, 1235–1248 (2024). https://doi.org/10.1007/s12530-023-09557-2

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  • DOI: https://doi.org/10.1007/s12530-023-09557-2

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