The aim of this paper is to perform a comparative study of feature reduction techniques that are most appropriate for the classification with k-nearest neighbor and tested with medical data. Medical data are normally high-dimensional in their nature. Their high dimensionality property can affect performance of the classification process. In this work, we perform various feature reduction techniques implemented with Matlab to decrease dimensions of data before the knearest neighbor classification step. From the experimented results, we found that best performance is obtained from using the PCA algorithm to reduce features of data. The comparison in terms of accuracy turns out that PCA and ROC feature reduction techniques can improve the classification prediction, whereas the t-test feature reduction has very limited effect over the classification accuracy.
Data classification mining is a method to find data generalization in a form of rules then used these rules to predict some unknown value in the future data. But in actual applications, the rules may be of low accuracy and the number of rules may be so overwhelmed that users could not efficiently apply them. Therefore, this research proposes the development of data classification algorithm with compact fuzzy association rules to optimize accuracy and interpretability of the model. To evaluate the performance of the proposed method, this research will compare accuracy of the classification model and the number of rules against 9 different data classification algorithms. The results showed that our CCFAR algorithm is comparable in terms of accuracy. When considering both accuracy and size of model, our algorithm is the best one.
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