ITM Web Conf.
Volume 37, 2021International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)
|Number of page(s)||8|
|Section||Innovative Technology for Sustainable Development|
|Published online||17 March 2021|
Lazy Learning Associative Classification with WkNN and DWkNN Algorithm
School of Computing Science and Engineering, VIT Chennai Campus, Tamil Nadu, 600127 India
* Corresponding author: firstname.lastname@example.org
One of the algorithms, which prudently denote better outcomes than the traditional associative classification systems, is the Lazy learning associative classification (LLAC), where the processing of training data is delayed until a test instance is received, whereas in eager learning, before receiving queries, the system begins to process training data. Traditional method assumes that all items within a transaction is same, which is not always true. This paper recommends a new framework called lazy learning associative classification with WkNN (LLAC_WkNN) which uses weighted kNN method with LLAC, that gives a subset of rules when LLAC is applied to the dataset. In order to predict the class label of the unseen test case, the weighted kNN (WkNN) algorithm is then applied to this generated subset. This creates the enhanced accuracy of the classifier. The WkNN also gives an outlier more weight. By applying Dual Distance Weight to LLAC named as LLAC_DWkNN, this limitation of WkNN is resolved. LLAC_DWkNN gives less weightage to outliers, which improve the accuracy of the classifier, further. This algorithm has been applied to different datasets and the experiment results demonstrate that the proposed method is efficient as compared to the traditional and other existing systems.
© The Authors, published by EDP Sciences, 2021
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