Nearest Centroid Classifier with Outlier Removal for Classification

Abstract

Classification is a part of data mining which is very useful for many purpose. There are so many method to classify, however a classification process need good source of data to deliver good performance. In the real world, not all data is good. Some of the data contains noise and outlier that can reduce classification performance. However, there are few research of classification with an outlier removal process, especially for Nearest Centroid Classifier Method. Nearest Centroid Classifier is a good yet simple classification method. However, Nearest Centroid Classifier suffers performance loss like the other classification method. The proposed methodology consists of two stages. First, preprocess the data with outlier removal, removes points which are far from the corresponding centroids, the center point of a class. Second, classify the outlier removed data or cleaned data. The experiment covers six data sets which have different characteristic. The results indicate that outlier removal as preprocessing method provide better result by 0.76% to 3.16% for improving Nearest Centroid Classifier performance on most data set.

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection

“…However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed [10]. Unfortunately, with only one centroid obtained for each class, the accuracy is comprised as a result, although realistic running time is achieved.…”