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

Author

Listed:
  • Kirsi Varpa
  • Kati Iltanen
  • Markku Siermala
  • Martti Juhola

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.

Suggested Citation

  • Kirsi Varpa & Kati Iltanen & Markku Siermala & Martti Juhola, 2017. "Attribute weighting with Scatter and instance-based learning methods evaluated with otoneurological data," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 2(3), pages 173-204.
    • Handle: RePEc:ids:ijdsci:v:2:y:2017:i:3:p:173-204
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