Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

Feature selection (FS) is a real-world problem that can be solved using optimization
techniques. These techniques proposed solutions to make a predictive model, which …

This paper thoroughly reviews and analyzes the main characteristics and application
portfolio of the so-called Harmony Search algorithm, a meta-heuristic approach that has …

Pattern recognition and machine learning techniques have been increasingly adopted in
adversarial settings such as spam, intrusion, and malware detection, although their security …

To obtain the optimal set of features in feature selection problems is the most challenging
and prominent problem in machine learning. Very few human-related metaheuristic …

This paper proposes a hybridized framework of Support Vector Machine (SVM) with K-
Nearest Neighbor approach for Indian stock market indices prediction. The objective of this …

Feature selection problem in data mining is addressed here by proposing a bi-objective
genetic algorithm based feature selection method. Boundary region analysis of rough set …

Misclassification costs of minority class data in real-world applications can be very high. This
is a challenging problem especially when the data is also high in dimensionality because of …

Microarray gene expression based medical data classification has remained as one of the
most challenging research areas in the field of bioinformatics, machine learning and pattern …