The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

In this paper, a theoretical and experimental analysis of linear combiners for multiple
classifier systems is presented. Although linear combiners are the most frequently used …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Random forests (RF) has become a popular technique for classification, prediction, studying
variable importance, variable selection, and outlier detection. There are numerous …

Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever
since have been a center of many discussions, fervently admired and condemned. Both …

This paper presents a combination of classifier selection and fusion by using statistical
inference to switch between the two. Selection is applied in those regions of the feature …

Stacking and bagging are widely used ensemble learning approaches that make use of
multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous …

In this paper, we present" k-means+ ID3", a method to cascade k-means clustering and the
ID3 decision tree learning methods for classifying anomalous and normal activities in a …

Mass appraisal is the systematic appraisal of groups of properties as of a given date using
standardized procedures and statistical testing. Mass appraisal is commonly used to …

This paper presents a comprehensive review of hybrid and ensemble-based soft computing
techniques applied to bankruptcy prediction. A variety of soft computing techniques are …