Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

Authors:
Mahdi Tabassian;Reza Ghaderi;Reza Ebrahimpour
Affiliations:
Faculty of Electrical and Computer Engineering, Babol University of Technology, Shariatee St., P.O. Box 484, Babol, Iran and School of Cognitive Sciences, Institute for Research in Fundamental Sci ...;Faculty of Electrical and Computer Engineering, Babol University of Technology, Shariatee St., P.O. Box 484, Babol, Iran;School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Niavaran Sq., P.O. Box 19395-5746, Tehran, Iran and Brain and Intelligent Systems Research Lab, Department of El ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 12
Cited 2

The transferable belief model

Artificial Intelligence
Combining the results of several neural network classifiers

Neural Networks
The Random Subspace Method for Constructing Decision Forests

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

Machine Learning
A Sparse Texture Representation Using Local Affine Regions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classifier combination using belief functions

Pattern Recognition Letters
Face Detection Using Mixture of MLP Experts

Neural Processing Letters
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Learning from data with uncertain labels by boosting credal classifiers

Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence

Expert Systems with Applications: An International Journal
A neural network classifier based on Dempster-Shafer theory

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Connectionist-based Dempster-Shafer evidential reasoning for data fusion

IEEE Transactions on Neural Networks

Wireless sensor network internal attacker identification with multiple evidence by dempster-shafer theory

ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Combining classifiers using nearest decision prototypes

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes' prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.