Weighted One-Class Classifier Ensemble Based on Fuzzy Feature Space Partitioning

2014 ◽  
Author(s):  
Bartosz Krawczyk ◽  
Michal Wozniak ◽  
Boguslaw Cyganek
Keyword(s):  
Feature Space ◽  
Classifier Ensemble ◽  
Space Partitioning ◽  
One Class Classifier ◽  
Feature Space Partitioning

A class discriminality measure based on feature space partitioning

1996 ◽  
Vol 29 (5) ◽  
pp. 873-887 ◽  
Author(s):  
AndréF. Kohn ◽  
Luis G.M. Nakano ◽  
Miguel Oliveira E Silva
Keyword(s):  
Feature Space ◽  
Space Partitioning ◽  
Feature Space Partitioning

scholarly journals Combined classifier based on feature space partitioning

2012 ◽  
Vol 22 (4) ◽  
pp. 855-866 ◽  
Author(s):  
Michał Woźniak ◽  
Bartosz Krawczyk
Keyword(s):  
Computer Experiments ◽  
Feature Space ◽  
Majority Voting ◽  
Space Partitioning ◽  
Voting Rule ◽  
Benchmark Datasets ◽  
Proposed Modification ◽  
The Individual ◽  
Combined Classifier ◽  
Feature Space Partitioning

This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.

Genetic programming based pattern classification with feature space partitioning

2001 ◽  
Vol 131 (1-4) ◽  
pp. 65-86 ◽  
Author(s):  
J.K. Kishore ◽  
L.M. Patnaik ◽  
V. Mani ◽  
V.K. Agrawal
Keyword(s):  
Genetic Programming ◽  
Pattern Classification ◽  
Feature Space ◽  
Space Partitioning ◽  
Feature Space Partitioning
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