Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/112
Title: A Comparative Analysis of K-NN and ANN Techniques in Machine Learning
Authors: Igiri, Chinwe Peace
Anyama, Oscar Uzoma
Silas, Abbasiama Ita
Sam, Iibi
Keywords: ANN; K-NN; Machine Learning; Prediction
Issue Date: Mar-2015
Publisher: International Journal of Engineering Research & Technology
Citation: Igiri, C. P., Anyama O. U., Silas A. I.& Sam I.(2015). A Comparative Analysis of K-NN and ANN Techniques in Machine Learning.
Series/Report no.: 4;3
Abstract: Different machine learning algorithms have been applied in various domains and have yielded good results. The application of a preferred technique to a named field is determined by the type of datasets and target goal in question. Although some researchers have shown different techniques resulting in the same prediction result. However, in this study, a critical analysis of the application of k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) has been carried out. This comparative analysis was done using the same datasets (English Premiership League) on this same platform (Rapid Miner). K-NN classification showed a prediction success of 53.33% while that of ANN was 70%. This proved that ANN is a better technique than k-NN for a polynomial label.
URI: http://localhost:8080/xmlui/handle/123456789/112
Appears in Collections:Computer Science

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