Abstract The use of Evolutionary Algorithms to perform data reduction tasks has become an
effective approach to improve the performance of data mining algorithms. Many proposals in …

The nearest neighbor (NN) classifier suffers from high time complexity when classifying a
test instance since the need of searching the whole training set. Prototype generation is a …

This paper offers a survey of recent work on particle swarm classification (PSC), a promising
offshoot of particle swarm optimization (PSO), with the goal of positioning it in the overall …

This work presents the design, implementation and validation of learning techniques based
on the kNN scheme for gesture detection in prosthetic control. To cope with high …

This paper describes a novel Particle Swarm Optimization (PSO)-based classification
algorithm with improved capabilities in comparison to several alternatives. The algorithm …

Currently, stock price forecasting is carried out using either time series prediction methods or
trend classifiers. The trend classifiers are designed to predict the behaviour of stock price's …

In this paper, we have studied the performance of a differential evolution (DE) classifier in
classifying data in noisy settings. We have also studied the performance in handling extra …

Due to the high accuracy of the K nearest neighbor algorithm in different problems, KNN is
one of the most important classifiers used in data mining applications and is recognized in …

Current systems of electromyographic prostheses are controlled by machine learning
techniques for gesture detection. Instance-based learning showed promising results …

Centroid-based clustering is a NP-hard optimization problem, and thus the common
approach is to search for cluster centers only for approximate solutions. Well-known centroid …