Efficiency of Feature Selection on Banana Classification

A. Anushya¹

Section:Research Paper, Product Type: Journal-Paper
Vol.5 , Issue.12 , pp.12-14, Dec-2019

Online published on Dec 31, 2019

Copyright © A. Anushya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This research compares the impact of feature selection in classification accuracy of Decision Tree on French Plantain (Nendran) dataset. At first, a five class home-made database is created and classification algorithm, Decision Tree is employed on dataset before and after feature selection via Rough set. The experiments are carried out on MATLAB. Results reveals that classification after feature selection produces 69% of classification accuracy which is enhanced than the before.

Key-Words / Index Term :
Classification, Decision Tree, C4.5, Feature Selection, Relative Reduct

References :
[1] N.S. Lele , “Image Classification Using Convolutional Neural Network”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.22-26, 2018.
[2] Ridhuna Rajan et.al., “Analysis and Dectection of Infected Fruit part using Improved K-means clustering and segmentation Techniques”, IOSR Journal of Computer Engineering (IOSR), Pp:37-41, 2015.
[3] Emny Harna Yossy et.al., “Mango fruit sortation system using Neural Networks and computer vision”, Procedia Computer Science, Volume: 116, Pp:599-603, 2017.
[4] Nandhan Thor et.al., “ Applying Machine Learning clustering and classification to predict Banana ripeness states and shelf life”, International Journal of Advanced food science and technology, Cloud Publications, Volume 2, issue :1,pp:20-25, 2017.
[5] Jonatha et.al., “ Citrus Friut quality classification using support vector machine”, International Journal of Advanced Engineering Research and Science, Vol:6, Issue:7, 2019.
[6] Fatima.M.A.Mazen et.al., “Ripeness classification of bananas using Artificial Neural Networks”, Arabian Journal for science and Engineering, 2019.
[7] Raj Kumar et.al, “Classifiction algorithms for Data Mining”, A Survey,International Journl of Innovations in Engineering and Technology,Vol.1, Issue 2 ,2012.
[8] Veronical, S.Moetini, “Towards the use of C4.5 Algorithm for classifying Banking Data set”, Integral, Vol.8. No.2, 2003.
[9] K.Thangavel et.al., “ Dimensionality reduction based on rough set theory: A review”, Applied softcomputing, volume 9, issue 1, 2009.