The collection of digital information by governments, corporations, and individuals has
created tremendous opportunities for knowledge-and information-based decision making …

Feature selection has been widely used in data mining and machine learning tasks to make
a model with a small number of features which improves the classifier's accuracy. In this …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people
based on membership to a category or a minority, without regard to individual merit. Rules …

Data mining is an increasingly important technology for extracting useful knowledge hidden
in large collections of data. There are, however, negative social perceptions about data …

Logistic regression with l1 regularization has been proposed as a promising method for
feature selection in classification problems. In this paper we describe an efficient interior …

Super learning is a general loss based learning method that has been proposed and
analyzed theoretically in van der Laan et al.(2007). In this article we consider super learning …

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called
as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a …

K-means is a widely used partitional clustering method. While there are considerable
research efforts to characterize the key features of K-means clustering, further investigation …

Measuring the performance of a given classifier is not a straightforward or easy task.
Depending on the application, the overall classification rate may not be sufficient if one, or …