Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

ANNs are nonlinear regression computational devices that have been used for over 45
years in classification and survival prediction in several biomedical systems, including colon …

Ambient air pollution is a pervasive global issue that poses significant health risks. Among
pollutants, ozone (O 3) is responsible for an estimated 1 to 1.2 million premature deaths …

In this work we compare the use of a particle swarm optimization (PSO) and a genetic
algorithm (GA)(both augmented with support vector machines SVM) for the classification 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 …

DNA microarray is a very active area of research in the molecular diagnosis of cancer.
Microarray data are composed of many thousands of features and from tens to hundreds of …

Ensemble methods try to improve performance via integrating different kinds of input data,
features, or learning algorithms. In addition to other areas, they are finding their applications …

Motivation: Many methods have been developed for selecting small informative feature
subsets in large noisy data. However, unsupervised methods are scarce. Examples are …

This paper reports on research to design an ensemble deep learning framework that
integrates fine-tuned, three-stream hybrid deep neural network (ie., Ensemble Deep …

Identifying the informative genes has always been a major step in microarray data analysis.
The complexity of various cancer datasets makes this issue still challenging. In this paper, a …