Comparative Analysis to Determine the Best Accuracy of Classification Methods


Warnia Nengsih(1*); Yuli Fitrisia(2); Mardhiah Fadhli(3);

(1) Politeknik Caltex Riau
(2) Politeknik Caltex Riau
(3) Politeknik Caltex Riau
(*) Corresponding Author

  

Abstract


The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each classification method used. The object that becomes simulation in this research is the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have good accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%

Keywords


SVM; K-NN; Decision Tree; Classification

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 438 times
PDF view: 152 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i2.1128.134-141 		  

Cite

How to cite item

References


Gou, J., Du, L.Zhang, Y. & Xiong, T. “A New Distance-weighted k-nearest Neighbor Classifier”, Journal of Information & Computational Science, 9 (6): 1429-1436. 2012

Domeniconi, C., Peng, J. & Gunopulos, D. "Locally adaptive metric nearest-oriented classification", IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (9): 1281–1285. 2002

Utku A, Hacer (Uke) Karacan, Yildiz O, Akcayol MA. Implementation of a New Recommendation System Based on Decision Tree Using Implicit Relevance Feedback. JSW. 2015 Dec 1; 10 (12): 1367-74.

Jadhav SD, Channe HP. Efficient recommendation system using decision tree classifier and collaborative filtering. Int. Res. J. Eng. Technol. 2016; 3: 2113-8.

Anyanwu MN, Shiva SG. Comparative analysis of serial decision tree classification algorithms. International Journal of Computer Science and Security. 2009 Jun; 3 (3): 230-40.

Gershman A, Meisels A, Lüke KH, Rokach L, Schclar A, Sturm A. A Decision Tree Based Recommender System. InIICS 2010 Jun 3 (pp. 170-179).

Pandey M, Sharma VK. A decision tree algorithm pertaining to the student performance analysis and prediction. International Journal of Computer Applications. 2013 Jan 1; 61 (13).

Priyama A, Abhijeeta RG, Ratheeb A, Srivastavab S. Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology. 2013 Jun; 3 (2): 334-7.

Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. "A Practical Guide to Support Vector Classification". Deptt of Computer Sci. National Taiwan Union, Taipei, 106, Taiwan http://www.csie.ntu.edu.tw/~cjlin 2007.

Banu GR. A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease. International Journal of Computer Sciences and Engineering. 2016; 4 (11): 111-5.

C.-W. Hsu and CJ Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13 (2): 415-425, 2002.

Baoli, L., Shiwen, Y. & Qin, L. "An Improved k-Nearest Neighbor Algorithm for Text Categorization, ArXiv Computer Science e-prints.2003

Bax, E. "Validation of nearest neighbor classifiers", IEEE Trans. Inform. Theory, 46: 2746–2752.2000

Li Maokuan, Cheng Yusheng, Zhao Honghai "Unlabeled data classification via SVM and k-means Clustering". Proceeding of the International Conference on Computer Graphics, Image and Visualization (CGIV04), 2004 IEEE.

Chitra, A. & Uma, S. "An Ensemble Model of Multiple Classifiers for Time Series Prediction", International Journal of Computer Theory and Engineering, 2 (3): 1793-8201.2010

Gil-Garcia, R. & Pons-Porrata, A. "A New Nearest Neighbor Rule for Text Categorization", Lecture Notes in Computer Science 4225, Springer, New York, 814–823.2006

Benetis, R., Jensen, C., Karciauskas, G. & Saltenis, S. "Nearest and Reverse Nearest Neighbor Queries for Moving Objects", The International Journal on Very Large Data Bases, 15 (3) : 229–250.2006

Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K., "Using KNN Model for Automatic Text Categorization", Soft Computing –A Fusion of Foundations, Methodologies and Applications 10 (5): 423–430.2006

Hastie, T., Tibshirani, R. & Friendman, J. "The Elements of Statistical Learning: Data Mining, Inference and Prediction", Springer, Stanford, CA, USA, ISBN: 978-0- 387 -84858-7.2009.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Warnia Nengsih

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.