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Lazy Learning Associative Classification with WkNN and DWkNN Algorithm
  • Preeti Tamrakar,
  • Syed Ibrahim S.P.
Preeti Tamrakar
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Syed Ibrahim S.P.
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Abstract

The Lazy learning associative classification (LLAC) is one of the associative classification methods, in which it delays the processing of training data until receives a test query, whereas, in eager learning, the system starts processing the training data before receiving queries. In this paper, the Lazy Learning Associative Classification with Weighted kNN (LLAC_WkNN) and Dual Weighted kNN (LLAC_DWkNN) are proposed. Where LLAC is applied on the dataset, that gives a subset of rules. Then weighted kNN (WkNN) algorithm is applied on this generated subset to predict the class label of the unseen test instance. This yields the improved accuracy of the classifier. The WkNN gives more weightage to outlier also. This limitation of WkNN is overcome by applying Dual Distance weighted kNN to LLAC. LLAC_DWkNN checks only k nearest neighbours, not all the large no of subsets for the subset evaluation and also gives less weight, which improves the accuracy of the classifier, further. The comparison results are shown in this paper of proposed algorithms with the existing associative classification methods and traditional methods in terms of classification accuracy.