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A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification

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Abstract

The experts’ decisions and evaluating the patients’ data are the most significant parts affecting the breast cancer analysis. For early breast cancer detection, numerous techniques of machine learning not only can assist in examining and diagnosis the medical data quickly but also decrease the potential errors that could be occurred due to inexpert or unskilled decision-makers. Support vector machine is one of the famous classifiers that has already made an important contribution to the field of cancer classification. However, configurations of different kernel function and their parameters can significantly affect the performance of the SVM classifier. To further improve the classification accuracy of the SVM classifier for breast cancer diagnosis, an intelligent cancer classification method is proposed based on selecting a feature subset and optimizing the relevant parameters (i.e., penalty factor parameter (\(c\)) and kernel parameter \(\gamma\)) of the SVM classifier concurrently through an intelligent algorithm using the Jaya algorithm. Then, this method (Jaya-SVM) was applied to precisely characterize the breast cancer dataset, including 699 samples, which are 458 and 241 for benign and malignant, respectively. Furthermore, to evaluate the effectiveness of the proposed Jaya-SVM classifier, it is compared in terms of the computational complexity and the classification accuracy with several combinatorial metaheuristic classifiers, namely the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and cuckoo search (CS) based-SVM. Apart from this, a Breast Cancer Coimbra Dataset taken from the UCI library is used to validate the effectiveness of the proposed method. The results are presented, explained, and conclusions are drawn.

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Abbreviations

SVM:

Support Vector Machine

DT:

Decision Tree

NB:

Naïve Bayes

k-NN:

K-nearest neighbors

GA:

Genetic Algorithm

PSO:

Particle Swarm Optimization

DE:

Differential Evolution

CS:

Cuckoo Search

\(R^{d}\) :

Input Space

\(x_{v}\) :

Sample Vector

\(y_{v}\) :

Class label of \(x_{v}\)

\(\varphi\) :

The Mapped Function

cccccc:

Hyperplane Weight Vector

b :

Bias

\(\xi_{v}\) :

Slack

C :

Penalty Factor

L r :

Lagrange Multipliers

\(k\left( {x_{v} ,x_{j} } \right)\) :

Kernel Function

\(k_{max}\) :

Maximum Iteration Number

\(N\) :

Papulation Size

X i,j :

jth Decision variable in the ith solution

\(X_{j}^{UB}\) :

Upper Bound

\(X_{j}^{LB}\) :

Lower Bound

\(f\left( X \right)\) :

Objective Function

\(X_{best}\) :

The Best Solution

\(X_{worst}\) :

The Worst Solution

\(X_{i,j}^{^{\prime}}\) :

The adjusted value of \(X_{i,j}\)

\(X_{i}^{^{\prime}}\) :

New solution

\(\hat{y}\left( p \right)\) :

Actual Output

\(y\left( p \right)\) :

Model estimation

\(P\left( {X_{i,j}^{^{\prime}} } \right)\) :

The Probability of the new solution

TP :

Number of True Positive

TN :

Number of True Negative

FP :

Number of False Positive

FN :

Number of False Negative

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Changsha, 410000, China

    Mohammed Alshutbi & Zhiyong Li

  2. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Moath Alrifaey

  3. School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Masoud Ahmadipour & Muhammad Murtadha Othman

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  1. Mohammed Alshutbi
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  2. Zhiyong Li
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Correspondence to Zhiyong Li or Moath Alrifaey.

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Alshutbi, M., Li, Z., Alrifaey, M. et al. A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification. Neural Comput & Applic 34, 16669–16681 (2022). https://doi.org/10.1007/s00521-022-07290-6

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