Estimating minimum effect with outlier selection

Estimating stability and selection index

Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...

Outlier Detection by Consistent Data Selection Method

Often the challenge associated with tasks like fraud and spam detection[1] is the lack of all likely patterns needed to train suitable supervised learning models. In order to overcome this limitation, such tasks are attempted as outlier or anomaly detection tasks. We also hypothesize that outliers have behavioral patterns that change over time. Limited data and continuously changing patterns ma...

Efficient Spectral Feature Selection with Minimum Redundancy

Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features i...

Detection of Outlier-Communities using Minimum Spanning Tree

Community (also known as clusters) is a group of nodes with dense connection. Detecting outlier-communities from database is a big desire. In this paper we propose a novel Minimum Spanning Tree based algorithm for detecting outlier-communities from complex networks. The algorithm uses a new community validation criterion based on the geometric property of data partition of the data set in order...