A Review of Heterogeneous Ensemble Methods
نویسنده
چکیده
Several ensemble methods have been proposed that can accommodate differing base model types. This document reviews the recent literature, and for each method, we identify (1) main contributions, (2) theoretical motivation, (3) empirical results and (4) relationships to other techniques.
منابع مشابه
Application of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملProtein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functio...
متن کاملEstimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods
Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across sub-populations— heterogeneous treatment effects —and how differences in the content of the treatment affects responses—the response to heterogeneous tr...
متن کاملHeterogeneous Ensemble Classification
The problem of multi-class classification is explored using heterogeneous ensemble classifiers. Heterogeneous ensembles classifiers are defined as ensembles, or sets, of classifier models created using more than one type of classification algorithm. For example, the outputs of decision tree classifiers could be combined with the outputs of support vector machines (SVM) to create a heterogeneous...
متن کاملEvaluation of Ensemble Classifiers for Intrusion Detection
One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are ana...
متن کامل