Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

Authors:
Mehdi Salkhordeh Haghighi;Abedin Vahedian;Hadi Sadoghi Yazdi
Affiliations:
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Venue:
Neural Processing Letters
Year:
2012

Citing 19
Cited 0

Neural networks and the bias/variance dilemma

Neural Computation
Bagging predictors

Machine Learning
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
On combining classifiers using sum and product rules

Pattern Recognition Letters
Random Forests

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
The ``Test and Select'' Approach to Ensemble Combination

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Experiments with Classifier Combining Rules

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Managing Diversity in Regression Ensembles

The Journal of Machine Learning Research
Using diversity of errors for selecting members of a committee classifier

Pattern Recognition
Advances of Research in Fuzzy Integral for Classifiers' fusion

SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Engineering multiversion neural-net systems

Neural Computation
A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems

Pattern Recognition
Creating ensembles of classifiers via fuzzy clustering and deflection

Fuzzy Sets and Systems
Dynamic and static weighting in classifier fusion

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article presents a new method to construct multiple classifier system by making diverse base classifiers using weight tuning. In the method presented, base classifiers are multilayer perceptions which creates diverse base classifiers using a three-step procedure. In the first step, base classifiers are trained for acceptable accuracy. In the second step, a weight tuning process tunes their weights such that each one can distinguish one class of input data from the others with highest possible accuracy. An evolutionary method is used to optimize efficiency of each base classifier to distinguish one class of input data in this step. In the third step, a new method combines the results of the base classifiers. As diversity is measured and monitored throughout the entire procedure, it is measured using a confusion matrix. Superiority of the proposed method is discussed using several known classifier fusion methods and known benchmark datasets.