The contemporary era demands a progress with respect to manual work or even semi-
machine dependence and the desired procession can be provided by Brain Computer …

The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

This paper proposes a novel hybrid genetic algorithm for feature selection. Local search
operations are devised and embedded in hybrid GAs to fine-tune the search. The operations …

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition
and machine learning, however, as a similar lazy classifier using local information for …

Besides optimizing classifier predictive performance and addressing the curse of the
dimensionality problem, feature selection techniques support a classification model as …

Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever
since have been a center of many discussions, fervently admired and condemned. Both …

Feature selection and instance selection are two important data preprocessing steps in data
mining, where the former is aimed at removing some irrelevant and/or redundant features …

Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic
lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) …

In the field of supervised machine learning, the quality of a classifier model is directly
correlated with the quality of the data that is used to train the model. The presence of …