Attribute weighting with Scatter and instance-based learning methods evaluated with otoneurological data

Kirsi Varpa, Kati Iltanen, Markku Siermala and Martti Juhola

International Journal of Data Science, 2017, vol. 2, issue 3, 173-204

Abstract: Treating all attributes as equally important during classification can have a negative effect on the classification results. An attribute weighting is needed to grade the relevancy and usefulness of the attributes. Machine learning methods were utilised in weighting the attributes. The machine learnt weighting schemes, weights defined by the application area experts and the weights set to 1 were tested on otoneurological data with the nearest pattern method of the decision support system ONE and the attribute weighted k-nearest neighbour method using one-vs-all (OVA) classifiers. The effects of attribute weighting on the classification performance were examined. The results showed that the extent of the effect the attribute weights had on the classification results depended on the classification method used. The weights computed with the Scatter method improved the total classification accuracy compared with the weights 1 and the expert-defined weights with ONE and the attribute weighted 5-nearest neighbour OVA methods.

Keywords: machine learning; attribute weighting; Scatter attribute importance evaluation method; instance-based learning; attribute weighted k-nearest neighbour method. (search for similar items in EconPapers)
Date: 2017
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