The week's pick
Reseach Article
Review on the Architecture, Algorithm and Fusion Strategies in Ensemble Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 108 - Number 8 |
| Year of Publication: 2014 |
| Authors: Shruti Asmita, K.k. Shukla |
10.5120/18932-0337
|
Shruti Asmita, K.k. Shukla . Review on the Architecture, Algorithm and Fusion Strategies in Ensemble Learning. International Journal of Computer Applications. 108, 8 ( December 2014), 21-28. DOI=10.5120/18932-0337
Abstract
Ensemble Learning is an approach in machine learning to find a predictive model taking into considerations the opinions of various experts. Groups of people can often make better decisions than individuals especially when group members come in with their own biases. This document presents a review on the possible architectures that can be used to build an ensemble model, the techniques in which the opinions of the experts could be combined to give a general improved model and the algorithms for implementing the Ensemble Learning. Comparison of architectures is done on the basis of diversity, classification accuracy and memory consumption. This can be helpful in choosing the options depending on the requirement. In the last part an analysis of ensemble learning algorithms on the basis on Bias and Variance is included.
References
- Gavin Brown, Encyclopaedia of Machine Learning Chapter No
- Pengyi Yang,Yee Hwa Yang, Bing B. Zhou and Albert Y. Zomaya, A review of ensemble methods in bioinformatics.
- P´adraig Cunningham , Technical Report UCD-CSI-2007-5. Ensemble Techniques.
- Henrik Boström, Feature vs. Classifier Fusion for Predictive Data Mining, a Case Study in Pesticide Classification
- De-qiang han, chong-zhao han, yi yang, Multi-class svm classifiers fusion based on evidence combination
- Rachid Benmokhtar and Benoit Huet, Classifier Fusion: Combination Methods For Semantic Indexing in Video Content
- L. Lam, Classifier combinations: implementations and theoretical issues, in: Proceedings of the First International Workshop on Multiple Classifier Systems
- A. F. R. Rahman, M. C. Fairhurst, Serial combination of multiple experts: a unified evaluation, Pattern Analysis and Applications.
- Micha? Wozniak, Manuel Grana, Emilio Corchado, A survey of multiple classifier systems as hybrid systems.
- G. T. Prasanna Kumari, A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier
- De-qiang han, chong-zhao han, yi yang, Multi-class svm classifiers fusion based on evidence combination .
- Khalid Jebari, Mohammed Madiafi , Selection Methods for Genetic Algorithms
- G. T. Prasanna Kumari, A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier
- Tom Dietterich, Rich Maclin, Bias Variance Tradeoff and Ensemble methods
- Xiujuan Chen, Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications
- Muhammad Nazir, Arfan Zaffar, Ayaz Hussain, M. Mirza, Efficient gender classification using optimization of hybrid classifiers using genetic algorithm
- Robi Polaker, Ensemble based systems in decision making.
- H. Ichihashi, T. Shirai, K. Nagasaka, and T. Miyoshi, Neuro-fuzzy ID3: A method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning.
- Dymitr Ruta and Bogdan Gabry, An Overview of Classifier Fusion Methods.
- T. H. Ho, J. J. Hull, S. N. Srihari, Decision Combination in Multiple Classifier System.
- K. Woods, W. P. Kegelmeyer, K. Bowyer, Combination of Multiple Classifiers Using Local Accuracy Estimates
- Li Tan, Yuanda Cao, A novel fusion method for semantic concept classification in video.
- Rachid Benmokhtar , Benoit Huet, Perplexity based evidential neural network classifier fusion using MPEG-7 low level visual features.
- Michal Wozniak, Manuel Grana, Emilio Corchado, A survey of multiple classifier systems as hybrid systems.
- Faizal M. Zaman, Hideo Hirose, Double SVM bagging, a new bagging with support vector machines.
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