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E LAS-Relief-A Novel Feature Selection Algorithm In Data mining

S.S. Baskar, L Arockiam

Abstract


Feature selection is vital task in any data mining pre processing procedure. This paper deals with the problem of estimating the quality features in the feature sets. The original feature estimating algorithm LAS-Relief algorithm can deal with discrete and continues attributes and it is limited to irrelevant feature removal. The new improved feature selection technique called E LAS-Relief deals with noisy and incomplete data sets. This paper shows that the novel algorithm E LAS-Relief outperforms on agriculture soil data sets for classification.


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References


Blessie E.C and Karthikeyan E, “RELIEF-DISC: An Extended RELIEF Algorithm Using iscretization Approach for Continuous Features”, Emerging Applications of Information Technology (EAIT), 2011 Second International Conference ,Feb 19-20,2011, pp 161 – 164.

Breiman L, Friedman J.H, Olshen R.A, Stone C.J,“Classification and Regression Trees”, Wadsforth International Group 1984.

Elena Deza & Michel Marie “Deza, Encyclopedia of Distances”, page 94, Springer, 2009. [4] Fan Wenbing, Wang Quanquan and Zhu Hui, “Feature Selection Method Based on Adaptive Relief Algorithm”, 3rd International Conference on Computer and Electrical Engineering (ICCEE 2010) IPCSIT, 2012, Vol. 53, No. 2 [5] Heum Park, Hyuk-Chul Kwon, “Extended Relief Algorithms in Instance-Based Feature Filtering”, Advanced Language Processing and Web Information Technology,2007. ALPIT 2007. Sixth International Conference, August 22-24, 2007, pp 123-128.

Kira K, Rendell L, “A practical approach to feature selection”, Proc 9th International Workshop on Machine Learning, 1992, pp 249-256.

Kononenko, I, “Estimating attributes: analysis and extensions of Relief”, In: L. De Raedt and F. Bergadano (eds.): Springer Verlag. Machine Learning: ECML-94, 1994, pp 171–182.

Mantaras R.L.: ID3 Revised: “A distance based creterian for attribute selection”, In: Proc.Int.Symp. Methodologies for Intelligent Systems. Charlotte,North Carolina. USA., Oct 1989.

Matthew E Stokes and Shyam Visweswaran, “Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease”, Bio Data Mining 2012, Vol 5, No 20.

Robnik Sikonjam, Kononenko I, “Theoretical and Empirical analysis of ReliefF and RReliefF”, Machine Learning, Vol 53, No 1, 2003, pp 23-69.

Quinlan R: Induction of decision trees. Machine learning 1: 81 106, 1986

Smyth P, and Goodman R.N,”Rule induction using information theory”, In. G.Piatetsky Shapiro & W. Frawley (eds.): Knowledge Discovery in Databases. MIT Press 1990

Sun Yi Jun, “Iterative relief for feature weighting algorithms, theories, and applications”, IEEE Trans on Pattern Analysis and Machine Intelligence, Vol 29, No 6, 2007, pp 1035-1051.

Yuxuan SUN, Xiaojun LOU, Bisai BAO,“A Novel Relief Feature Selection Algorithm Based on Mean Variance Model”, Journal of Information & computational Science Vol.8, No 16, 2011, pp 3921-3929.




DOI: http://dx.doi.org/10.6084/ijact.v2i12.329

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