Skip to main content
SpringerLink
Log in

Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In the biomedical research field, feature selection plays the predominant role in prediction of diseases. The main objective of this paper is to predict cancer from microarray gene expression data by proposing two feature selection algorithms, namely (1) differential evolution with fuzzy rough set feature selection and (2) ant colony optimization with fuzzy rough set feature selection algorithms, which solve the multi-objective optimization problems. The first algorithm represents the hybridization of differential evolution and fuzzy rough set and aims to select the global optimal features by applying the fuzzy rough evaluation function as the fitness function. The second algorithm, i.e., hybridization of ant colony optimization and fuzzy rough set, selects global optimal features by applying the fuzzy rough evaluation function as the fitness function. The performance of proposed two features selection algorithms is evaluated with various classification metrics, which are computed from decision tree classifier using tenfold cross-validation. Five datasets are applied to analyze the performance of the feature selection algorithms. The datasets used are diffuse large B cell lymphoma, breast cancer, Leukemia and small round blue-cell tumors which are cancer datasets. In addition, a non-medical dataset, namely Gisette, is also used to demonstrate the generalization capability of the proposed algorithms. The metrics used for comparison are, namely, accuracy, precision, recall, f-measure, specificity, processing time and receiver operating characteristics. The performance comparison evidenced improved performances for the proposed algorithms. Similar to the hybridization of differential evolution and ant colony optimization with fuzzy rough set, particle swarm optimization can be extended in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abualigah LMQ (2019) Feature selection and enhanced Krill Herd algorithm for text document clustering. Studies in computational intelligence, vol 816. Springer, Cham, Switzerland, pp 1–165. https://doi.org/10.1007/978-3-030-10674-4

    Book  Google Scholar 

  • Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

    Google Scholar 

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh NES (2018a) A combination of objective functions and hybrid Krill Herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018b) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018c) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018d) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering. Intell Decis Technol 12(1):3–14

    Article  Google Scholar 

  • Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04629-4

    Article  Google Scholar 

  • Akhtar MS, Gupta D, Ekbal A, Bhattacharyya P (2017) Feature selection and ensemble construction: a two-step method for aspect based sentiment analysis. Knowl-Based Syst 125:116–135

    Article  Google Scholar 

  • Benala TR, Mall R (2018) DABE: differential evolution in analogy-based software development effort Estimation. Swarm Evol Comput 38:158–172

    Article  Google Scholar 

  • Benjamin SCW, Shantanu HJ, Boris AG, Paul Thompson M (2017) Machine learning on high dimensional shape data from subcortical brain surfaces: a comparison of feature selection and classification methods. Pattern Recogn 63:731–739

    Article  Google Scholar 

  • Chen B, Chen L, Chen Y (2013) Efficient ant colony optimization for image feature selection. Sig Process 93:1566–1576

    Article  Google Scholar 

  • Chuang LY, Yang CS, Wuc KC, Yang CH (2011) Gene selection and classification using Taguchi chaotic binary particle swarm optimization. Expert Syst Appl 38:13367–13377

    Article  Google Scholar 

  • Das AK, Goswami S, Chakrabarti A, Chakraborty B (2017) A new hybrid feature selection approach using feature association map for supervised and unsupervised classification. Expert Syst Appl 88:81–94

    Article  Google Scholar 

  • Jadhav S, He H, Jenkins K (2018) Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput 66:541–553

    Article  Google Scholar 

  • Lakshmi BN, Indumathi TS, Nandini R (2016) A study on C.5 decision tree classification algorithm for risk predictions during pregnancy. Proc Technol 24:1542–1549

    Article  Google Scholar 

  • Lu H, Chen J, Yan K, Jin Qun, Xue Y, Gao Z (2017) A hybrid feature selection algorithm for gene expression data Classification. Neurocomputing 256:56–62

    Article  Google Scholar 

  • Meenachi L, Ramakrishnan S (2018) Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier. Healthc Technol Lett 5:130–135

    Article  Google Scholar 

  • Meenachi L, Ramakrishnan S (2020) Random global and local optimal search algorithm based subset generation for diagnosis of cancer. Curr Med Imaging 16:249. https://doi.org/10.2174/1573405614666180720152838

    Article  Google Scholar 

  • Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A (2017) A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Inform Med Unlocked 9:246–254

    Article  Google Scholar 

  • Myszkowski Pawel B, Olech Lukasz P, Laszczyk M, Skowronski ME (2018) Hybrid differential evolution and greedy algorithm (DEGR) for solving multi-skill resource-constrained project scheduling problem. Appl Soft Comput 62:1–14

    Article  Google Scholar 

  • Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2020) MLACO: a multi-label feature selection algorithm based on ant colony optimization. Knowl Based Syst 192:105285

    Article  Google Scholar 

  • Paul D, Su R, Romain M, Sebastien V, Pierre V, Isabelle G (2017) Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Comput Med Imaging Graph 60:42–49

    Article  Google Scholar 

  • Rao H, Shi X, Rodrigue AK, Feng J, Xia Y, Elhoseny M, Yuan X, Gu L (2019) Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput J 74:634–642

    Article  Google Scholar 

  • Riza LS, Janusz A, Bergmeir C, Cornelis C, Herrera F, Slezak D, Benitez JM (2014) Implementing algorithms of rough set theory and fuzzy rough set theory in the R package RoughSets. Inf Sci 287:68–89

    Article  Google Scholar 

  • Salem H, Attiya G, El-Fishawy N (2017) Classification of human cancer diseases by gene expression profiles. Appl Soft Comput 50:124–134

    Article  Google Scholar 

  • Sheeja TK, Sunny Kuriakose A (2018) A novel feature selection method using fuzzy rough sets. Comput Ind 97:111–116

    Article  Google Scholar 

  • Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2019) Moth-flame optimization algorithm:variants and applications. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04570-6

    Article  Google Scholar 

  • Shehab M, Alshawabkah H, Abualigah L, Nagham AM (2020) Enhanced a hybrid moth-flame optimization algorithm using new selection schemes. Eng Comput. https://doi.org/10.1007/s00366-020-00971-7

    Article  Google Scholar 

  • Tabakhi S, Najafi A, Ranjbar R, Moradi P (2015) Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing 168:1024–1036

    Article  Google Scholar 

  • Tian M, Gao X, Dai C (2017) Differential evolution with improved individual-based parameter setting and selection strategy. Appl Soft Comput 56:286–297

    Article  Google Scholar 

  • Vivekanandan T, Ch Sriman Narayana Iyengar N (2017) Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput Biol Med 90:125–136

    Article  Google Scholar 

  • Wei JM, Wang SQ, Yuan XJ (2010) Ensemble rough hypercuboid approach for classifying cancers. IEEE Trans Knowl Data Eng 22:381–391

    Article  Google Scholar 

  • Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172–186

    Article  MathSciNet  Google Scholar 

  • Zhang T, Ding B, Zhao X, Yue Q (2018) A fast feature selection algorithm based on swarm intelligence in acoustic defect detection. IEEE Access 6:28848–28858

    Article  Google Scholar 

  • Zheng K, Wang X (2018) Feature selection method with joint maximal information entropy between features and class. Pattern Recogn 77:20–29

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India

    L. Meenachi & S. Ramakrishnan

Authors
  1. L. Meenachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Ramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Meenachi.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest, financially or otherwise.

Ethical approval

This article does not contain any studies with the participants of humans or animals.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meenachi, L., Ramakrishnan, S. Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification. Soft Comput 24, 18463–18475 (2020). https://doi.org/10.1007/s00500-020-05070-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05070-9

Keywords

Search

Navigation