Feature Extraction and Independent Subset Generation Using Genetic Algorithm for Improved Classification


  • P. Rathna Sekhar Research Scholar, Department of Computer Science and Engineering University College of Engineering, Osmania University, Hyderabad.
  • B. Sujatha Assistant Professor, Department of Computer Science and Engineering University College of Engineering, Osmania University, Hyderabad.


Feature Extraction, Feature Selection, Genetic Algorithm, Classification, High Dimensional Data, Independent Features


The number of traits that can be retrieved from the vast amounts of data of various forms available today is enormous. This is especially true for text data, which has benefited from the proliferation of multimedia applications. Using every available feature for each of the classification tasks can be not just time-consuming but also performance-detrimental. When a number of measurements, or features, have been acquired from a set of objects in a standard statistical pattern recognition problem, feature extraction is a frequent technique employed prior to classification. To achieve this, it is necessary to create a mapping from original representation space to a new space in which the classes may be more readily distinguished from one another. Selecting an appropriate feature set to characterize the patterns being classed is a common requirement in challenges involving knowledge discovery and pattern classification. This is because the classifier's performance and the cost of classification are both highly sensitive to the features used for the classifier's construction. To identify near-optimal solutions to such optimization issues, Genetic Algorithms (GA) present an appealing strategy. High-quality approaches to optimization and exploration issues can be quickly and easily generated using genetic algorithms, which rely on bioinspired operators including mutation, crossover, and selection. The genetic algorithm is an approach to solve optimization problems with constraints and without them, inspired by natural selection, the mechanism behind biological evolution. The genetic algorithm iteratively improves upon a pool of candidate solutions. A genetic learning and evolution model is used to pick or extract features while simultaneously designing a classifier. In this research Independent Subset Generation using Genetic Algorithm for Improved Classification (ISG-GA-IC) model is proposed for accurate selection of independent features for enhancing the classification levels. In this paper, we provide the results of our studies on the use of evolutionary algorithms for feature extraction and selection in high-dimensional data sets.


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C. Peng, X. Wu, W. Yuan, X. Zhang, Y. Zhang and Y. Li,

"MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 2, pp. 621-632, 1 March-April 2021, doi: 10.1109/TCBB.2019.2921961.

Y. Wang, B. Song, P. Zhang, N. Xin and G. Cao, "A Fast

Feature Fusion Algorithm in Image Classification for Cyber Physical Systems," in IEEE Access, vol. 5, pp. 9089-9098, 2017, doi: 10.1109/ACCESS.2017.2705798.

Y. Zhang, Q. Wang, D.-W.Gong and X.-F. Song,

"Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection", Pattern Recognit., vol. 93, pp. 337-352, Sep. 2019.

G. J. Ansari, J. H. Shah, M. C. Q. Farias, M. Sharif, N.

Qadeer and H. U. Khan, "An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm," in IEEE Access, vol. 9, pp. 54923-54937,2021,doi: 10.1109/ACCESS.2021.3071169.

K. Nag and N. R. Pal, "Feature Extraction and Selection for

Parsimonious Classifiers withMultiobjective Genetic Programming," in IEEE Transactions on Evolutionary Computation, vol. 24, no. 3, pp. 454-466, June 2020, doi: 10.1109/TEVC.2019.2927526.

A.Khan, A. S. Qureshi, N. Wahab, M. Hussain and M. Y.

Hamza, "A recent survey on the applications of genetic programming in image processing" in arXiv preprint arXiv:1901.07387, 2019.

Y. Bi, B. Xue and M. Zhang, "An automated ensemble

learning framework using genetic programming for image classification", Proc. Genet. Evol.Comput.Conf., pp. 365-373, 2019.

C. S. Ooi, M. H. Lim and M. S. Leong, "Self-Tune Linear

Adaptive-Genetic Algorithm for Feature Selection," in IEEE Access, vol. 7, pp. 138211-138232, 2019, doi: 10.1109/ACCESS.2019.2942962.

Y. Ou, S. -Q. Ye, L. Ding, K. -Q. Zhou and A. M. Zain,

"Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN," in IEEE Access, vol. 10, pp. 72037-72050, 2022, doi: 10.1109/ACCESS.2022.3188689.

M. Sharif, M. A. Khan, M. Faisal, M. Yasmin and S. L.

Fernandes, "A framework for offline signature verification system: Best features selection approach", Pattern Recognit. Lett., vol. 139, pp. 50-59, Nov. 2020.

Y. Zhang, D.-W.Gong, X.-Z.Gao, T. Tian and X.-Y. Sun,

"Binary differential evolution with self-learning for multi-objective feature selection", Inf. Sci., vol. 507, pp. 67-85, Jan. 2020.

Y. Bi, B. Xue and M. Zhang, "Genetic Programming With

a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification," in IEEE Transactions on Cybernetics, vol. 51, no. 4, pp. 1769-1783, April 2021, doi: 10.1109/TCYB.2020.2964566.

B. P. Evans, "Population-based ensemble learning with

tree structures for classification", 2019.

S. Li, K. Zhang, Q. Chen, S. Wang and S. Zhang, "Feature

Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm," in IEEE Access, vol. 8, pp. 139512-139528, 2020, doi: 10.1109/ACCESS.2020.3012768.

X. Y. Kek, C. S. Chin and Y. Li, "Multi-Timescale

Wavelet Scattering With Genetic Algorithm Feature Selection for Acoustic Scene Classification," in IEEE Access, vol. 10, pp. 25987-26001, 2022, doi: 10.1109/ACCESS.2022.3156569.

Y. Kataoka, T. Nakashika, R. Aihara, T. Takiguchi and Y.

Ariki, "Selection of an optimum random matrix using a genetic algorithm for acoustic feature extraction," 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 2016, pp. 1-6, doi: 10.1109/ICIS.2016.7550890.

Y. Jinxia, C. Zixing and D. Zhuohua, "Improved method

for the feature extraction of laser scanner using genetic clustering," in Journal of Systems Engineering and Electronics, vol. 19, no. 2, pp. 280-285, April 2008, doi: 10.1016/S1004-4132(08)60079-1.

S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN:

Towards real-time object detection with region proposal networks", Proc. Adv. Neural Inf. Process.Syst. (NIPS), pp. 91-99, 2015.

K. Nag and N. R. Pal, "A multiobjective genetic

programming-based ensemble for simultaneous feature selection and classification", IEEE Trans. Cybern., vol. 46, no. 2, pp. 499-510, Feb. 2016.

K. Nag and N. R. Pal, "Genetic programming for

classification and feature selection" in Evolutionary and Swarm Intelligence Algorithms, Cham, Switzerland:Springer, pp. 119, 2019.

B. Xue, M. Zhang, W. N. Browne and X. Yao, "A survey

on evolutionary computation approaches to feature selection", IEEE Trans. Evol. Comput., vol. 20, no. 4, pp. 606-626, Aug. 2016.

Y. Xu, Y. Sun, J. Wan, X. Liu and Z. Song, "Industrial big

data for fault diagnosis: Taxonomy review and applications", IEEE Access, vol. 5, pp. 17368-17380, 2017.

L. Kou, C. Liu, G.-W.Cai, J.-N. Zhou, Q.-D.Yuan and S.-

M. Pang, "Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches", IET Power Electron., vol. 13, no. 6, pp. 1236-1245, 2020.

Q. Zhou, P. Yan, H. Liu and Y. Xin, "A hybrid fault

diagnosis method for mechanical components based on ontology and signal analysis", J. Intell. Manuf., vol. 30, no. 4, pp. 1693-1715, Apr. 2019.

R. Ramos, C. D. Acosta, P. J. R. Torres, E. I. S. Mercado,

G. B. Baez, L. A. Rifón, et al., "An approach to multiple fault diagnosis using fuzzy logic", J. Intell. Manuf., vol. 30, no. 1, pp. 429-439, Jan. 2019.

G. Wang, F. Zhang, B. Cheng and F. Fang, "DAMER: A

novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis", J. Intell. Manuf., vol. 32, no. 1, pp. 1-20, Jan. 2021.

K.-Q. Zhou, L.-P. Mo, J. Jin and A. M. Zain, "An

equivalent generating algorithm to model fuzzy Petri net for knowledge-based system", J. Intell. Manuf., vol. 30, no. 4, pp. 1831-1842, Apr. 2019.

General Process of GA in Feature Selection




How to Cite

P. . Rathna Sekhar and B. . Sujatha, “Feature Extraction and Independent Subset Generation Using Genetic Algorithm for Improved Classification”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 503–512, Mar. 2023.



Research Article