Authors:
Elham Bavafaye Haghighi
and
Mohammad Rahmati
Affiliation:
Amirkabir University of Technology, Iran, Islamic Republic of
Keyword(s):
Mapping to Multidimensional Optimal Regions, Multi-classifier, PCA, Code Assignment, Feature Selection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
Mapping to Multidimensional Optimal Regions (M2OR) is a special purposed method for multiclass classification task. It reduces computational complexity in comparison to the other concepts of classifiers. In order to increase the accuracy of M2OR, its code assignment process is enriched using PCA. In addition to the increment in accuracy, corresponding enhancement eliminates the unwanted variance of the results from the previous version of M2OR. Another advantage is more controllability on the upper bound of V.C. dimension of M2OR which results in a better control on its generalization ability. Additionally, the computational complexity of the enhanced-optimal code assignment algorithm is reduced in training phase. By the other side, partitioning the feature space in M2OR is an NP hard problem. PCA plays a key role in the greedy feature selection presented in this paper. Similar to the new code assignment process, corresponding greedy strategy increases the accuracy of the enhanced M2
OR.
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