Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …

Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

Ensemble learning has been shown to be an effective approach to solve multi-label
classification problem. However, most existing ensemble learning methods do not consider …

Machine learning algorithms are becoming very efficient in intrusion detection systems with
their real time response and adaptive learning process. A robust machine learning model …

Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Credit risk assessment is usually regarded as an imbalanced classification task solved by
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …