In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-Nearest Neighbour (k-NN) method and Naïve Bayes (NB) methods. Furthermore, Multinomial Logistic Regression (MNLR) was tested for the dataset. HAH-SVM with the linear kernel achieved clearly the best accuracy being 76.9% which was a good result with the dataset tested. From the other classification methods HAH-k-NN with cityblock metric, HAH-NB and MNLR methods achieved above 60% accuracy. Around 77% accuracy is a good result compared to previous researches with the same dataset.
Genetic algorithms have been utilized in many complex optimization and simulation tasks because of their powerful search method. In this research we studied whether the classification performance of the attribute weighted methods based on the nearest neighbour search can be improved when using the genetic algorithm in the evolution of attribute weighting. The attribute weights in the starting population were based on the weights set by the application area experts and machine learning methods instead of random weight setting. The genetic algorithm improved the total classification accuracy and the median true positive rate of the attribute weightedk-nearest neighbour method using neighbour’s class-based attribute weighting. With other methods, the changes after genetic algorithm were moderate.
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