The explosion of data which is happening now must be utilized to support decision making
both in terms of business and other matters. Data which are becoming assets today needs to …

Typical feature selection methods select a global feature subset that is applied over all
regions of the sample space. In localized feature selection (LFS), each region of the sample …

With the extensive increase of the amount of data, such as text categorization, genomic
microarray data, bio-informatics and digital images, there are more and more challenges in …

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

Feature selection happens to be an important step in many classification tasks. Its aim is to
reduce the number of features and at the same time to try to maintain or even improve the …

In many data analysis tasks, one is often confronted with very high dimensional data.
Feature selection is an effective method to solve the problem with high dimensional data …

Choosing a proper classifier for one specific data set is important in practical application.
Automatic classifier selection (CS) aims to recommend the most suitable classifiers to a new …

본 논문에서는 퍼지 k-NN 과 reconstruction error 에 기반을 둔 feature selection 을 이용한 lazy
분류기 설계를 제안하였다. Reconstruction error 는 locally linear reconstruction 의 평가 …

Many classification algorithms degrade their learning performance while irrelevant features
are introduced. Feature selection is a process to choose an optimal subset of features and …

In this paper, we propose a novel method to select the most informativesubset of features,
which has little redundancy andvery strong discriminating power. Our proposed approach …