The prediction accuracy of short-term load forecast (STLF) depends on prediction model
choice and feature selection result. In this paper, a novel random forest (RF)-based feature …

Identifying gear damage categories, especially for early faults and combined faults, is a
challenging task in gear fault diagnosis. This paper proposes a new multidimensional hybrid …

In real world situations every model has some weaknesses and will make errors on training
data. Given the fact that each model has certain limitations, the aim of ensemble learning is …

Human-centric applications of a single thermal sensor array (TSA) have performed
extremely well in many areas. However, most of these works have not yet reached the real …

In this paper entropy based methods are compared and used to measure structural diversity
of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby …

Diversity is deemed a crucial concept in the field of multiple classifier systems, although no
exact definition has been found so far. Existing diversity measures exhibit some issues, both …

This paper proposes a face recognition method using artificial immune networks based on
principal component analysis (PCA). The PCA abstracts principal eigenvectors of the image …

RESEARCH QUESTIONS: 1. Dealing with long term historical data of spatio-temporal is
always challenging. SVM ensemble and other methods are used to handle long term …

Over the past few decades, the remarkable prediction capabilities of ensemble methods
have been used within a wide range of applications. Maximization of base-model ensemble …

Generating ensembles from multiple individual classifiers is a popular approach to raise the
accuracy of the decision. As a rule for decision making, majority voting is a usually applied …