Data are rarely perfect. Whether the problem is data entry errors or rare events. Outliers have two opposing properties. They can be noises that disturb regression and classification task. On the other hand, they can provide valuable information about rare phenomena, which can lead to knowledge discovery. This paper proposes a hybrid algorithm including K Nearest Neighbor and Support Vector Machine (KSVM) that detects outliers by taking the advantages of the two intelligent techniques, Support Vector Machine (SVM) and K Nearest Neighbour (KNN). Also a global efficiency measure introduced to compare different methods. Finally, a comparison between KNN, SVM, and KSVM is conducted using detection rate, accuracy rate, false alarm rate, true negative rate and the proposed global efficiency measure based on benchmark data called Milk data.
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