Surrogate-assisted firefly algorithm for breast cancer detection

Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost.

[1]  Rommel G. Regis,et al.  Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions , 2014, IEEE Transactions on Evolutionary Computation.

[2]  Zhijian Wu,et al.  Firefly Algorithm With Luciferase Inhibition Mechanism , 2019, IEEE Access.

[3]  Heng Zhang,et al.  A particle swarm optimization algorithm for mixed-variable optimization problems , 2021, Swarm Evol. Comput..

[4]  Ben Niu,et al.  Feature Selection Using a Reinforcement-Behaved Brain Storm Optimization , 2019, ICIC.

[5]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[6]  Ye Tian,et al.  A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[7]  Wenbing Zhao,et al.  Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm , 2017, 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC).

[8]  Yixuan Li,et al.  An ensemble learning based prediction strategy for dynamic multi-objective optimization , 2020, Appl. Soft Comput..

[9]  Aboul Ella Hassanien,et al.  Firefly Optimization Algorithm for Feature Selection , 2015, BCI.

[10]  Feng Liu,et al.  Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning , 2016, Neurocomputing.

[11]  Moloud Abdar,et al.  Using Decision Trees in Data Mining for Predicting Factors Influencing of Heart Disease , 2015 .

[12]  R. L. Hardy.  Multiquadric equations of topography and other irregular surfaces , 1971 .

[13]  Giorgio Valentini,et al.  Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.

[14]  Moloud Abdar,et al.  Using PSO Algorithm for Producing Best Rules in Diagnosis of Heart Disease , 2017, 2017 International Conference on Computer and Applications (ICCA).

[15]  Kandala N. V. P. S. Rajesh,et al.  Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine , 2017, Comput. Biol. Medicine.

[16]  Xin Yao,et al.  A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.

[17]  Amr Tolba,et al.  Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics , 2018, Neural Computing and Applications.

[18]  John Doherty,et al.  Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles , 2019, IEEE Transactions on Evolutionary Computation.

[19]  Sang Won Yoon,et al.  Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..

[20]  Aimin Zhou,et al.  A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[21]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[22]  Moloud Abdar,et al.  Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Prediction , 2017, 2017 International Conference on Computer and Applications (ICCA).

[23]  Zhijian Wu,et al.  Enhancing firefly algorithm with courtship learning , 2021, Inf. Sci..

[24]  Hui Wang,et al.  Firefly algorithm with random attraction , 2016, Int. J. Bio Inspired Comput..

[25]  Feng Wang,et al.  An Estimation of Distribution Algorithm for Mixed-Variable Newsvendor Problems , 2020, IEEE Transactions on Evolutionary Computation.

[26]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[27]  M. Gadala,et al.  Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem , 2013 .

[28]  Ying Tan,et al.  Surrogate-assisted hierarchical particle swarm optimization , 2018, Inf. Sci..

[29]  Y. Y. Huang,et al.  A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy , 2014, Optim. Methods Softw..

[30]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[31]  Marek A. Perkowski,et al.  A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection , 2015, 2015 IEEE International Symposium on Multiple-Valued Logic.

[32]  Qifu Wang,et al.  Sparsity-promoting polynomial response surface: A new surrogate model for response prediction , 2014, Adv. Eng. Softw..

[33]  Yang Yu,et al.  A two-layer surrogate-assisted particle swarm optimization algorithm , 2014, Soft Computing.

[34]  Dana Bazazeh,et al.  Comparative study of machine learning algorithms for breast cancer detection and diagnosis , 2016, 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA).

[35]  Liang Hu,et al.  Feature redundancy term variation for mutual information-based feature selection , 2020, Applied Intelligence.

[36]  Zidong Wang,et al.  Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis , 2018 .

[37]  William Stafford Noble,et al.  Support vector machine , 2013 .

[38]  Hui Wang,et al.  Firefly algorithm with neighborhood attraction , 2017, Inf. Sci..