Lazy Learning Associative Classification with Hybrid Feature Selection
Preeti Tamrakar1, S. P. Syed Ibrahim2
1Preeti Tamrakar, Research Scholar, School of Computing Science and Engineering, VIT Chennai Campus, Chennai, 600127, India.
2S. P. Syed Ibrahim, Professor, School of Computing Science and Engineering, VIT, Chennai Camous, Chennai 600127, India.
Manuscript received on 15 April 2019 | Revised Manuscript received on 19 May 2019 | Manuscript published on 30 May 2019 | PP: 299-303 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3112058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Lazy learning associative classification is one of the associative classification methods in which it delays the generalization of the training data until it receives a test query. It lacks in performance due to availability of many features in the dataset. All the features do not contribute classification system. It is important to choose the most appropriate features to identify the class of unseen test tuples. This paper shows how hybrid feature selection method can be applied to lazy learning associative classification to overcome this issue. The proposed method integrates a forward selection and backward elimination approach of feature selection methods that leads to good selection of attributes and better accuracy. Experimental results of the proposed system are visibly positive in comparison to the traditional and existing associative classification methods.
Index Terms: Associative Classification, Attribute (Feature) Selection Method, Forward Selection and Backward Elimination, Lazy Learning.
Scope of the Article: Classification