Ensemble learning methods for decision making : status and future prospects
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Authors
Ali, Shahid
Tirumala, Sreenivas Sremath
Sarrafzadeh, Hossein
Tirumala, Sreenivas Sremath
Sarrafzadeh, Hossein
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Grantor
Date
2015-07
Supervisors
Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
Support Vector Machine (SVM)
classifier
diversity
ensemble systems
algorithms
machine learning
classifier
diversity
ensemble systems
algorithms
machine learning
ANZSRC Field of Research Code (2020)
Citation
Ali, S., Tirumala, S. S., & Sarrafzadeh, A. (2015). Ensemble learning methods for decision making : Status and future prospects. IEEE (Ed.), 13th IEEE International Conference on Machine Learning and Cybernetics (IEEE ICMLC 2015) (pp.1-11)
Abstract
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 to supervise their strengths and weaknesses, leading to best possible decision in general. Ensemble based machine learning is a solution of minimizing risk in decision making. Bagging, boosting, stacked generalization and mixture of expert methods are the most popular techniques to construct ensemble systems. For the purpose of combining outputs of class labels, weighted majority voting, behaviour knowledge space and border count methods are used to construct independent classifiers and to achieve diversity among the classifiers which is important in ensemble learning. It was found that an ideal ensemble method should work on the principle of achieving six paramount characteristics of ensemble learning; accuracy, scalability, computational cost, usability, compactness and speed of classification. In addition, the ideal ensemble method would be able to handle large huge image size and long term historical data particularly of spatial and temporal. In this paper we reveal that ensemble models have obtained high acceptability in terms of accuracy than single models. Further, we present an analogy of various ensemble techniques, their applicability, measuring the solution diversity, challenges and proposed methods to overcome these challenges without diverting from the original concepts.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Institute of Electrical and Electronics Engineers (IEEE)
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