Outlier Detection in Test Samples using Standard Deviation and Unsupervised Training Set Selection

Outlier detection is a technique to identify and remove significantly different data from the more correct consistent in set. can have negative impact on classification clustering performance; that should be identified removed improve efficiency. Regardless of whether classifying classifies an outlier correctly, very notion identifying as great significance. In this paper, new approach proposed for within test set along with unsupervised training selection. The selected used two-step classification. After set, closest cluster sample using Euclidean distance measure. Then, concepts standard deviation mean value. results showed by evaluating each accuracy classifiers enhanced after elimination data.