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Inclusion of Efficient Rules in PRISM Algorithm for Data Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 45 |
| Year of Publication: 2019 |
| Authors: Yogesh Wanjari, Sanjay Nagpure, Gokul Chute, Yogeshwari Kamble |
10.5120/ijca2019918547
|
Yogesh Wanjari, Sanjay Nagpure, Gokul Chute, Yogeshwari Kamble . Inclusion of Efficient Rules in PRISM Algorithm for Data Classification. International Journal of Computer Applications. 182, 45 ( Mar 2019), 5-11. DOI=10.5120/ijca2019918547
Abstract
Data classification is the process of classify data into different types, forms or any other separate classes. Data may be classified for a different of reasons, including ease of access, to observe with regulatory requirements, and to meet various other business or personal intention. In some cases, data classification is a regulatory requirement, as data must be searchable and recoverable within specified time durations. the focus of this paper is on the description of rule based classification and ensemble learning as well as the discussion on some existing methods and techniques. In our proposed approach we are using PRISM algorithm for rule induction. Based on induced rule, test data will be classified. In this paper we proposed Maximized the classification accuracy, Minimize the error rate and also Minimize the classification time. After that experimental evaluation will be performed with basic PRISM algorithm and will show comparative analysis of basic PRISM algorithm and other data classification algorithm such as SVM, Decision Tree, Perceptron Model and Logistic Regression. After comparing these classification algorithms, we found that Maximum accuracy using PRISM algorithm.
References
- Cendrowska, J., 1987. “PRISM: An algorithm for inducing modular rules”. International Journal of Man-Machine Studies, 27(4), pp.349-370.
- Hadi, W.E., Issa, G. and Ishtaiwi, A., 2017. “ACPRISM: Associative classification based on PRISM algorithm”. Information Sciences, 417, pp.287-300. ELSEVIER.
- Hadi, W.E., Aburub, F. and Alhawari, S., 2016. “A new fast associative classification algorithm for detecting phishing websites”. Applied Soft Computing, 48, pp.729-734. ELSEVIER.
- Hong, Tzung-Pei, and Shian-Shyong Tseng. "Two-phase PRISM learning algorithms." In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, vol. 4, pp. 3895-3899.
- Bruni, R. and Bianchi, G., 2015. “Effective classification using a small training set based on discretization and statistical analysis”. IEEE Transactions on Knowledge and Data Engineering, 27(9), pp.2349-2361.
- Cunhe, L. and Chenggang, W., 2010, May. “A new semi-supervised support vector machine learning algorithm based on active learning”. In Future Computer and Communication (ICFCC), 2010 2nd International Conference on (Vol. 3, pp. V3-638). IEEE.
- Martens, D., Baesens, B.B. and Van Gestel, T., 2009. “Decompositional rule extraction from support vector machines by active learning”. IEEE Transactions on Knowledge and Data Engineering, 21(2), pp.178-191.IEEE.
- Kesavaraj, G. and Sukumaran, S., 2013, July. A study on classification techniques in data mining. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
- Jia, Y.S., Jia, C.Y. and Qi, H.W., 2005, August. “A new nu-support vector machine for training sets with duplicate samples”. In Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Vol. 7, pp. 4370-4373). IEEE.
- Kotsiantis, S.B., Zaharakis, I. and Pintelas, P., 2007. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160, pp.3-24.
- Dietterich, T.G., 2000, June. “Ensemble methods in machine learning”. In International workshop on multiple classifier systems (pp. 1-15). Springer, Berlin, Heidelberg.
- Abdelhamid, N., 2015. “Multi-label rules for phishing classification”. Applied Computing and Informatics, 11(1), pp.29-46. ELSEVIER.
- N. Abdelhamid, A .A . Jabbar, F. Thabtah, “Associative classification common research challenges”. in: 2016 45th Int. Conf. Parallel Process. Work., 2016, pp. 432–437. IEEE.
- D.T. Pham, S. Bigot, S.S. Dimov, “RULES-5: a rule induction algorithm for classification problems involving continuous attributes”. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 217 (2003) 1273–1286. ELSEVIER.
- Osisanwo, F.Y., Akinsola, J.E.T., Awodele, O., Hinmikaiye, J.O., Olakanmi, O. and Akinjobi, J., 2017. Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), pp.128-138.
- Liu, H., 2015. Rule Based Systems for Classification in Machine Learning Context, Doctoral dissertation, University of Portsmouth.
- Yogesh Wanjari, Sanjay Nagpure, Gokul Chute, Yogeshwari Kamble, Review on Inclusion of Efficient Rules in PRISM algorithm for Data Classification IJRAR-International Jurnal of Research and Analyatical Reviews, Volume.6, Issue 1, Page No pp.640-642, February-2019.
- Wanjari, Y.W., Mohod, V.D., Gaikwad, D.B. and Deshmukh, S.N., 2014, October. Automatic news extraction system for Indian online newspapers. In Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization (pp. 1-6). IEEE.
- Santra, A.K. and Christy, C.J., 2012. Genetic algorithm and confusion matrix for document clustering. International Journal of Computer Science Issues (IJCSI), 9(1), p.322.
- https://en.wikipedia.org/wiki/Machinelearning
- https://jupyter.readthedocs.io/en/latest/
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