This paper proposes a simple yet effective algorithm for optimizing a trained linear classifier
to minimize the average feature acquisition cost at test-time. For good classification …

Linear classifier is the most popular and widely used classifier for most classification
problems, due to its simplicity and efficiency. The decision is made based on the value of …

Feature selection plays an important role in pattern classification. Its purpose is to remove
redundant features from data set as many as possible. The presence of useless features …

Feature selection is a technique to improve the classification accuracy of classifiers and a
convenient data visualization method. As an incremental, task oriented, and model-free …

Data preprocessing describes any type of processing methods performed on raw data to
prepare it for another processing procedure. Commonly used as a preliminary data mining …

With the rapid development of information techniques, the dimensionality of data in many
application domains, such as text categorization and bioinformatics, is getting higher and …

Classification is a supervised machine learning procedure in which the effective model is
constructed for prediction. The accuracy of classification mainly depends on the type of …

Feature selection refers to a critical preprocessing of machine learning to remove irrelevant
and redundant data. According to feature selection methods, sufficient samples are usually …

Feature selection is an important data preprocessing step which is performed before a
learning algorithm is applied. The issue that has to be taken into consideration when …

In this paper, we propose a new feature evaluation method that forms the basis for feature
ranking and feature selection. The method starts by generating a number of feature subsets …