Costs-sensitive classification in multistage classifier with fuzzy observations of object features

Authors:
Robert Burduk
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Year:
2011

Citing 11
Cited 2

On the multistage Bayes classifier

Pattern Recognition
The Use of Background Knowledge in Decision Tree Induction

Machine Learning
Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics

Machine Learning
Cost-sensitive pruning of decision trees

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Test-Cost Sensitive Classification on Data with Missing Values

IEEE Transactions on Knowledge and Data Engineering
On the Mean Accuracy of Hierarchical Classifiers

IEEE Transactions on Computers
Active Feature-Value Acquisition

Management Science
Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm

Journal of Artificial Intelligence Research
Classification error in Bayes multistage recognition task with fuzzy observations

Pattern Analysis & Applications
Randomness and fuzziness in bayes multistage classifier

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I

Comparison of cost for zero-one and stage-dependent fuzzy loss function

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Combining diverse one-class classifiers

HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the paper the problem of cost in hierarchical classifier is presented. Assuming that both the tree structure and the feature used at each non-terminal node have been specified, we present the expected total cost for two cases. The first one concerns the non fuzzy observation of object features, the second concerns the fuzzy observation. At the end of the work the difference between expected total cost of fuzzy and non fuzzy data is determined. Obtained results relate to the locally optimal strategy of Bayes multistage classifier.