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Combat with Class Overlapping in Software Defect Prediction Using Neighbourhood Metric

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Abstract

The characteristics of data is a open problem which has been tended perceived in data analysis in machine learning research from last decades. The researcher defined some measures to identify the characteristics of the dataset by applying data complexity measures to find the fitness for purpose. The presence of class overlapping in data-sets, significantly affect performance of the classifiers. Data complexity measures provide quantitative insight in quality of the data set and overlapping existent in it. Machine learning techniques are also utilized by several researchers on healthcare datasets in software defect prediction. In this paper, our aim is to evaluates the effectiveness of new overlap measure: Near Enemy Ratio, and its effect on complexity measures and performance of the classifier. The new ration is based on nearest instances to the target instance. The experimental result offers insights in usefulness of the method and help us decide whether this solution should be applied on a particular data-set or not.

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References

  1. Kuncheva LI, Rodrguez JJ. A weighted voting framework for classifiers ensembles. Knowl Inform Syst. (in press). 2013. https://doi.org/10.1007/s10115-012-0586-6.

  2. Fawcett T, Provost F. Adaptive Fraud Detection. Data Min Knowl Discovery. 1997;1(3):291–316.

    Article  Google Scholar 

  3. Sáez JA, Galar M, Luengo J, Herrera F. Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowl Inform Syst (in press). 2013. https://doi.org/10.1007/s10115-012-0570-1.

  4. Baumgartner R, Somorjai RL. Data complexity assessment in undersampled classification. Pattern Recognit Lett. 2006;27:1383–9.

    Article  Google Scholar 

  5. Bernadó-Mansilla E, Ho TK. Domain of competence of XCS classifier system in complexity measurement space. IEEE Trans Evol Comput. 2005;9(1):82–104.

    Article  Google Scholar 

  6. Basu M, Ho TK. Data complexity in pattern recognition. Berlin: Springer; 2006.

    Book  MATH  Google Scholar 

  7. Sánchez JS, Mollineda RA, Sotoca JM. An analysis of how training data complexity affects the nearest neighbor classifiers. Pattern Anal Appl. 2007;10:189–201.

    Article  MathSciNet  Google Scholar 

  8. José Salvador Sánchez, Pla Filiberto, Ferri Francesc J. Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognit Lett. 1997;18(6):507–13.

    Article  Google Scholar 

  9. Bernadó-Mansilla E, Ho TK. Domain of competence of XCS classifier system in complexity measurement space. IEEE Trans Evol Comput. 2005;9(1):82–104.

    Article  Google Scholar 

  10. Mollineda RA, Sánchez JS, Sotoca JM. Data characterization for effective prototype selection. In: Proceedings of the 2nd Iberian Conference on Pattern Recognition and Image Analysis, Springer, 2005; pp 27–34.

  11. Orriols-Puig A, Macià N, Ho TK. Documentation for the Data Complexity Library in C++, Technical Report, La Salle - Universitat Ramon Llull 2010.

  12. Zhang ML, Zhou ZH. Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit. 2007;40:2038–48.

    Article  MATH  Google Scholar 

  13. Hoekstra Aarnoud, Duin Robert PW. Investigating redundancy in feed-forward neural classifiers. Pattern Recognit Lett. 1997;18(11):1293–300.

    Article  Google Scholar 

  14. Janez Demšar. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006;7:1–30.

    MathSciNet  MATH  Google Scholar 

  15. Gupta Shivani, Gupta Atul. A set of measures designed to identify overlapped instances in software defect prediction. Computing. 2017;99(9):889–914.

    Article  MathSciNet  Google Scholar 

  16. He Haibo, Garcia Edwardo A. Learning from imbalanced data. Knowl Data Eng IEEE Trans. 2009;21(9):1263–84.

    Article  Google Scholar 

  17. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P. Witten, The WEKA data mining software: an update; SIGKDD Explor 2009; 10–18.

  18. Platt J. Fast training of support vector Machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods–support vector learning, Chap 12. MIT Press, 1998; pp 169–185.

  19. Yu S, Zhou ZH, Steinbac M, Hand DJ, Steinberg D. Top 10 algorithms in data mining. Knowl Inform Syst. 2007;14(1):1–37.

    Google Scholar 

  20. John GH, Langley P. Estimating continuous distributions in Bayesian classifiers. In: Eleventh conference on uncertainty in artificial intelligence, San Mateo, 1995; pp 338–345.

  21. Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco: Morgan Kaufmann; 2005.

    MATH  Google Scholar 

  22. Cover TM, Hart PE. Nearest Neighbor Pattern Classification. IEEE Trans Information Theory. 1967;13:21–7.

    Article  MATH  Google Scholar 

  23. Cortes C, Vapnik V. Support vector networks. Mach Learn. 1995;20:273–97.

    Article  MATH  Google Scholar 

  24. Quinlan J C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufman, 1992.

  25. Joaquín D, Isaac T, Salvador G, Francisco H. Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms. IEEE Trans Syst Man Cybern Part B. 2012;42(5):1383–97.

    Article  Google Scholar 

  26. Gongde G et al. KNN model-based approach in classification. OTM confederated international conferences on the move to meaningful internet systems. Springer, Berlin, Heidelberg, 2003.

  27. Zang B et al. An improved KNN algorithm based on minority class distribution for imbalanced dataset. Comput Symp (ICS), 2016 International. IEEE, 2016.

  28. Kotsiantis Sotiris B, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Emerging Artificial Intell Appl Comput Eng. 2007;160:3–24.

    Google Scholar 

  29. Zhu X, Wu X, Yang Y Error detection and impact-sensitive instance ranking in noisy datasets. AAAI; 2004.

  30. Hunt EB, Janet M, Philip JS. Experiments in induction 1966.

  31. Holte Robert C. Very simple classification rules perform well on most commonly used datasets. Mach Learn. 1993;11(1):63–90.

    Article  MATH  Google Scholar 

  32. Wu X, Zhu X. Mining with noise knowledge: error aware data mining. IEEE Trans Syst Man Cybernet-Part A. 2008;38(4):917–32.

    Article  Google Scholar 

  33. Liu C. Partial discriminative training for classification of overlapping classes in document analysis. Int J Document Anal Recognit. 2008;11(2):53–65.

    Article  Google Scholar 

  34. Shivani G, Atul G Domain of Competency of Classifiers on Overlapping Complexity of Datasets Using Multi-label Classification with Meta-Learning. Adv Comput Intell Commun Technol. 2019. Springer Singapore, 2021.

  35. Andrews S Learning from ambiguous examples, 2007;68(07).

  36. Tang Y, Gao J. Improved classification for problem involving overlapping patterns. IEICE Trans Inform Syst. 2007;90(11):1787–95.

    Article  Google Scholar 

  37. Gupta Shivani. Classifiers recommendation system for overlapped software defect prediction using multi-label framework. J Adv Res Dyn Control Syst. 2020;12(1472–1478):2020.

    MathSciNet  Google Scholar 

  38. Visa S, Ralescu A Learning imbalanced and overlapping classes using fuzzy sets, in Proceedings of the ICML, 2003;3.

  39. Xiong H, Wu J, Liu L. Classification with class overlapping: a systematic study. In: The 2010 international conference on E-business intelligence, 2010; pp. 491-497.

  40. Ojima Y, Horiuchi S, Ishikawa F Model-based Data-Complexity Estimator for Deep Learning Systems. In: IEEE International conference on artificial intelligence testing (AITest). Oxford, United Kingdom. 2021;2021:1–8.

  41. Gupta Shivani, Gupta Atul. Dealing with noise problem in machine learning data-sets: a systematic review. Proc Comput Sci. 2019;161:466–74.

    Article  Google Scholar 

  42. Hoerl Roger, Jensen Willis, de Mast Jeroen. Understanding and addressing complexity in problem solving. Q Eng. 2021;33(4):612–26. https://doi.org/10.1080/08982112.2021.1952230.

    Article  Google Scholar 

  43. Sarker IH. Machine learning: algorithms, Real-world applications and research directions. SN COMPUT SCI. 2021;2:160. https://doi.org/10.1007/s42979-021-00592-x, 2021.

  44. Alsaeedi A, Khan MZ. Software defect prediction using supervised machine learning and ensemble techniques: a comparative study. J Softw Eng Appl. 2019;12(05):85–100.

    Article  Google Scholar 

  45. Khan B, Naseem R, Muhammad F, Abbas G, Kim S. An empirical evaluation of machine learning techniques for chronic kidney disease prophecy. IEEE Access. 2020;8:55012–22.

    Article  Google Scholar 

  46. Gupta Shivani, Gupta Atul. Handling class overlapping to detect noisy instances in classification. Knowl Eng Rev. 2018;33: e8.

    Article  Google Scholar 

  47. Santos Miriam Seoane, et al. A unifying view of class overlap and imbalance: key concepts, multi-view panorama, and open avenues for research. Inform Fus. 2023;89(2023):228–53.

    Article  Google Scholar 

  48. Dai Qi, Liu Jian-wei, Shi Yong-hui. Class-overlap undersampling based on Schur decomposition for Class-imbalance problems. Expert Syst Appl. 2023;221(2023): 119735.

    Article  Google Scholar 

  49. Prince Mini, Prathap PM Joe. An imbalanced dataset and class overlapping classification model for big data. Comput Syst Sci Eng. 2023;44(2):1009–24.

    Article  Google Scholar 

  50. Lina G et al. A comprehensive investigation of the impact of class overlap on software defect prediction. IEEE transactions on software engineering 2022.

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Authors and Affiliations

  1. School of Computer Science Engineering, Vellore Institute of Technology, Chennai, India

    Shivani Gupta

  2. Department of Computer science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Richa

  3. School of Electronics Engineering, Vellore Institute of Technology, Chennai, India

    Ranjeet Kumar & Kusum Lata Jain

  4. School of Computer & Communication Engineering, Manipal University Jaipur, Jaipur, India

    Ranjeet Kumar & Kusum Lata Jain

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This article is part of the topical collection “Advances in Machine Vision and Augmented Intelligence” guest edited by Manish Kumar Bajpai, Ranjeet Kumar, Koushlendra Kumar Singh and George Giakos.

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Gupta, S., Richa, Kumar, R. et al. Combat with Class Overlapping in Software Defect Prediction Using Neighbourhood Metric. SN COMPUT. SCI. 4, 695 (2023). https://doi.org/10.1007/s42979-023-02082-8

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