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Generalized Discussion over Classification Algorithm under Supervised Machine Learning Paradigm

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Sheenam Goel, Mamta
10.5120/ijca2018916858

Sheenam Goel and Mamta. Generalized Discussion over Classification Algorithm under Supervised Machine Learning Paradigm. International Journal of Computer Applications 180(32):29-34, April 2018. BibTeX

@article{10.5120/ijca2018916858,
	author = {Sheenam Goel and Mamta},
	title = {Generalized Discussion over Classification Algorithm under Supervised Machine Learning Paradigm},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {32},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {29-34},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume180/number32/29252-2018916858},
	doi = {10.5120/ijca2018916858},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper, Learning is an important parameter for developing machines that are intelligent as well as efficient. The studies of virtual environment with parameters that are encountered periodically during run time of algorithm are studied effectively under machine learning domains. Optimized decision making floors the base of pattern recognition as a subarea of machine learning. Being influenced from theories of genetic sciences, cognitive learning, the efficiency of algorithms developed in this area is effectively exploited. However ensuring the adaptability of machines to have artificial thinking & generate optimum results when applied to domain of computer vision, various techniques have been correlated by means of diverse paradigm approach. Estimation of efficiency of one algorithm over other suitably forecast the optimum though not the best solution in terms of minimized error rate when applied to a problem statement. Although machine learning involves automation, but it imbibes human guidance to generate effective results and provides generalization on system so that they perform well on data patterns hidden in a problem space. The paper focuses on classical discussion over different techniques to be applied on areas of machine learning like classification and regression, two important aspects of learning over binary and multiclass problems. However the applicability of statistical models have grewed up with the deficiencies of lacking reasoning capabilities, handling categorical data and missing values with the major drawback of skipping reasoning and generalizing ability. So the advent of learning algorithm have revolutionize the performance of system by imbibing artificial data with knowledge applied from experience i.e. training machines in order to generate correct results. Classification problems have been widespread in both binary and multiclass datasets. So having employs this supervised approach for appropriate handling of such kind of problems and determine the effectiveness of each with its shortcomings are generalized in the paper. The paper will be focused on explanatory techniques of classification their discussion domains of applications so that when they are applied on data set, they generate effective results.

References

  1. M Sahare , H Gupta , “A Review of Multi-Class Classification for Imbalanced Data”, International Journal of Advanced Computer Research ,ISSN 2249-7277 ,Vol. 2,2012.
  2. Y Singh,PK Bhatia,” A Review of Studies on Machine learning techniques”,IJCSS,Vol.1.
  3. B Wujek,P Hall,F Güneș ,” Best Practices for Machine Learning Applications”, Paper SAS2360-2016.
  4. M Gupta,N Aggarwal,” Classification Techniques Analysis”, National Conference on Computational Instrumentation CSIO Chandigarh,March 2010.
  5. M Allahyari,S Pouriyeh, “A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques”, Xiv:1707.02919v2 [cs.CL] , Jul 2017.
  6. A Talwar,Y Kumar,” Machine Learning: An artificial intelligence methodology”, .International Journal Of Engineering And Computer Science ,ISSN:2319-7242 ,Vol. 2 ,2013.
  7. Y-y-Song,Y LU ,” Decision tree methods: applications for classification & prediction”, Shanghai Arch Psychiatry,2015.
  8. A Gupta,S Joshi,” Study of Classification Algorithms used in Sentiment Analysis”, International Journal of Computer Science and Information Technologies, Vol. 5 , 2014.
  9. Shotaro Matsumoto, Hiroya Takamura, and Manabu Okumura “Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees” Advances in Knowledge Discovery and Data Mining ,301-311, 9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005. Proceedings.
  10. T.-N. Do and F. Poulet, ``Random local SVMs for classifying largedatasets,'' in Future Data and Security Engineering SE-1, vol. 9446. Cham, Switzerland: Springer, 2015, pp. 3_15.
  11. B.Waske and J. A. Benediktsson, ``Fusion of support vector machines for classification of multisensor data,'' IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3858_3866, Dec. 2007
  12. ] K.-R.M¨uller, S. Mika, G. R¨atsch, K. Tsuda, and B. Sch¨olkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181–201, 2001
  13. ] P. Laskov, C. Sch¨afer, and I. Kotenko. Intrusion detection in unlabeled data with quarter-sphere support vector machines. In Proc. DIMVA, pages 71–82, 2004.
  14. Kamal Nigam, John Lafferty ,Andrew McCallum,“ Using Maximum Entropy for Text Classification” IJCAI-99 Workshop on Machine Learning for Information Filtering, 1999, Pages 61-67 – Max Entropy
  15. L. Torrey and J. Shavlik, Handbook of Research on Machine Learning Applications and Trends. Hershey, PA: IGI Global, 2010.
  16. S. Haykin. Neural Networks: A comprehensive foundation, 2nd Ed. Prentice-Hall, 1999. D.P. Helmbold, D.D.E. Long, T.L. Sconyers, and B. Sherrod. Adaptive disk spin-down for mobile computers. Mobile Networks and Applications, 5(4):285–297, 2000.
  17. ] S. Mendelson and A. Smola, editors. Advanced Lectures on Machine Learning, volume 2600 of LNAI. Springer, 2003.
  18. W. Zang, P. Zhang, C. Zhou, and L. Guo, ``Comparative study between incremental and ensemble learning on data streams: Case study,'' J. Big Data, vol. 1, no. 1, p. 5, 2014.
  19. M. K Warmuth, J. Liao, G. R¨atsch, Mathieson. M., S. Putta, and C. Lemmem. Support Vector Machines for active learning in the drug discovery process. Journal of Chemical Information Sciences, 43(2):667–673, 2003Decision tree methods: applications for classification and prediction
  20. V.N. Vapnik. The nature of statistical learning theory. Springer Verlag, New York, 1995. U. von Luxburg, O. Bousquet, and G. R¨atsch, editors. Advanced Lectures on Machine Learning, volume 3176 of LNAI. Springer, 2004.

Keywords

Learning, Recognition, Classification, Binary and Multiclass