Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Solar irradiation data are imperatively required for any solar energy-based project. The non-
accessibility and uncertainty of these data can greatly affect the implementation …

Feature selection is a key pre-processing technique for classification which aims at
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …

The speech signal is like the black box of human beings where much information is hidden.
The treatment of this signal provides us with the speaker's identity. In a way, it is similar to an …

The classical genetic algorithm utilizes random population initialization, an unguided
crossover operator, and an unguided mutation operator for feature selection. However, this …

Microarray data plays a major role in diagnosing and treating cancer. In several microarray
data sets, many gene fragments are not associated with the target diseases. A solution to the …

The selection of an optimal feature subset from all available features in the data is a vital
task of data pre-processing used for several purposes such as the dimensionality reduction …

Feature selection has always been a critical step in pattern recognition, in which
evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used …

Nowadays, data dimensions have increased depending on the developments in information
and measurement technologies. Due to the high dimensionality, it is necessary to use pre …

This paper explores the effectiveness of Particle Swarm Classification (PSC) for a
classification task in the field of educational data mining. More specifically, it proposes PSC …