Skip to main content
Log in

ELM-based gene expression classification with misclassification cost

Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cost-sensitive learning is a crucial problem in machine learning research. Traditional classification problem assumes that the misclassification for each category has the same cost, and the target of learning algorithm is to minimize the expected error rate. In cost-sensitive learning, costs of misclassification for samples of different categories are not the same; the target of algorithm is to minimize the sum of misclassification cost. Cost-sensitive learning can meet the actual demand of real-life classification problems, such as medical diagnosis, financial projections, and so on. Due to fast learning speed and perfect performance, extreme learning machine (ELM) has become one of the best classification algorithms, while voting based on extreme learning machine (V-ELM) makes classification results more accurate and stable. However, V-ELM and some other versions of ELM are all based on the assumption that all misclassifications have same cost. Therefore, they cannot solve cost-sensitive problems well. To overcome the drawback of ELMs mentioned above, an algorithm called cost-sensitive ELM (CS-ELM) is proposed by introducing misclassification cost of each sample into V-ELM. Experimental results on gene expression data show that CS-ELM is effective in reducing misclassification cost.

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

Access this article

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

References

  1. Han J, Kamber M (2011) Data mining concepts and techniques. Morgan Kanfnann, San Francisco CA

    Google Scholar 

  2. Zheng EH, Li P, Song ZH (2006) SVM-based Cost Sensitive Mining. Information and Control 3:294299

  3. Chow CK (1970) On optimum recognition error and reject tradeoff. IEEE Trans Inf Theory 1:41–46

    Google Scholar 

  4. Foggia P, Sansone C, Tortorella F et al (1999) Mulit-classification: reject criteria for the bayesian combiner. Pattern Recognit 32:1435–1447

    Google Scholar 

  5. Stefano CD, Sansone C, Vento M (2008) To reject or not to reject: that is the question-answer in case of neural classifiers. IEEE Trans SMC 30(1):84–94

    Google Scholar 

  6. Landgrebe T, Taxdmj CW, Paclik P et al (2006) The interaction between classification and reject performance for distance-based reject-option classifiers. Pattern Recognit Lett 8:908–917

    Google Scholar 

  7. Zheng EH et al. (2009) SVM-BASED credit card fraud detection with reject cost and class-dependent error cost. Proceedings of the PAKDD’ 09 Workshop: data mining when classes are imbalanced and errors have cost. Rangsit Campus: Thammasat Printing House, 2009: 50–58

  8. Zheng EH (2010) Cost sensitive data mining based on support vector machines: theories and applications. Control and Decision 25(2):191–195

    Google Scholar 

  9. Kubat M, Holte R, Matwin S (1997) Learning when negative examples abound. Machine learning: ECML-97, Lecture Notes in Artificial Intelligence. Springer. 1224:146–153

  10. Chan PK, Stolfo SJ (2001) Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. Proceedings of the 4th international conference on knowledge discovery and data mining. 164–168

  11. Weiss GM, Provost F (2003) Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res 19:315–354

    MATH  Google Scholar 

  12. Japkowicz N (2000) The class imbalance problem: significance and strategies. Proceedings of the 2000 international conference on artificial intelligence: special track on inductive learning, Las Vegas

  13. Elkan C (2001) The foundation of cost-sensitive learning. Proceedings of the 17th international joint conference on artificial intelligence, Washington. 239–246

  14. Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77

    Google Scholar 

  15. Liu XY, Wu J, Zhou ZH (2009) Exploratory under-sampling for class-imbalance learning. IEEE Trans Syst Man Cybern-Part B: Cyber 39(2):539–550

    Google Scholar 

  16. Ling C, Li C (1998) Data mining for direct marketing problems and solutions. Proceedings of the 4th international conference on knowledge discovery and data mining, New York. 73–79

  17. Drummond C, Holte R (1998) Exploiting the cost in sensitivity of decision tree splitting criteria. Proceedings of the 17th international conference on machine learning. Stanford University. 73–79

  18. Fan W, Stolfo S, Zhang J, Chan P (1999) Adacost: misclassification cost-sensitive boosting. Proceedings of the 16th international conference on machine learning. Bled, Slovenia. 97–105

  19. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    MATH  MathSciNet  Google Scholar 

  20. Xiao JH, Pan KQ, Wu JP, Yang SZ (2001) A study on SVM for fault diagnosis. J Vib Measurement Diagn 21(4):258–262

    Google Scholar 

  21. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  22. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Proceedings of international joint conference on neural networks. 25–29

  23. Cao JW, Lin ZP, Huang GB, Liu N (2011) Voting based extreme learning machine. Inf Sci 185(1):66–77

    MathSciNet  Google Scholar 

  24. Michie D, Spiegelhalter J, Taylor CC Machine learning neural and statistical classification. http://www.nccup.pt/liacc/ML/statlog/data.html. 199

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (no. 61272315, no. 60842009, and no. 60905034), Zhejiang Provincial Natural Science Foundation (no. Y1110342, no. Y1080950) and the Pao Yu-Kong and Pao Zhao-Long Scholarship for Chinese Students Studying Abroad.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui-juan Lu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lu, Hj., Zheng, Eh., Lu, Y. et al. ELM-based gene expression classification with misclassification cost. Neural Comput & Applic 25, 525–531 (2014). https://doi.org/10.1007/s00521-013-1512-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1512-x

Keywords

Navigation