Feature selection plays a critical role in biomedical data mining, driven by increasing feature
dimensionality in target problems and growing interest in advanced but computationally …

With the advent of high dimensionality, adequate identification of relevant features of the
data has become indispensable in real-world scenarios. In this context, the importance of …

Relevant feature identification has become an essential task to apply data mining algorithms
effectively in real-world scenarios. Therefore, many feature selection methods have been …

Modern biomedical data mining requires feature selection methods that can (1) be applied
to large scale feature spaces (eg 'omics' data),(2) function in noisy problems,(3) detect …

This paper considers feature selection for data classification in the presence of a huge
number of irrelevant features. We propose a new feature-selection algorithm that addresses …

RELIEF is considered one of the most successful algorithms for assessing the quality of
features. In this paper, we propose a set of new feature weighting algorithms that perform …

Selecting relevant features for support vector machine (SVM) classifiers is important for a
variety of reasons such as generalization performance, computational efficiency, and feature …

Automatic, accurate and online fault detection of heating ventilation air conditioning (HVAC)
subsystems, such as chillers, is highly demanded in building management system (BMS) to …

Kernel methods and rough sets are two general pursuits in the domain of machine learning
and intelligent systems. Kernel methods map data into a higher dimensional feature space …

Feature selection aims to eliminate redundant or irrelevant variables from input data to
reduce computational cost, provide a better understanding of data and improve prediction …