Cost-Sensitive splitting and selection method for medical decision support system

Authors:
Konrad Jackowski;Bartosz Krawczyk;Michał Woźniak
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 10
Cited 3

Cost-sensitive concept learning of sensor use in approach and recognition

Proceedings of the sixth international workshop on Machine learning
The Use of Background Knowledge in Decision Tree Induction

Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Data clustering: a review

ACM Computing Surveys (CSUR)
A Method for Inductive Cost Optimization

EWSL '91 Proceedings of the European Working Session on Machine Learning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas

Pattern Analysis & Applications
Introduction to Machine Learning

Introduction to Machine Learning
Cost-sensitive ensemble of support vector machines for effective detection of microcalcification in breast cancer diagnosis

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II

Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage

ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Classifier ensemble for an effective cytological image analysis

Pattern Recognition Letters
A survey of multiple classifier systems as hybrid systems

Information Fusion

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a cost-sensitive modification of the Adaptive Splitting and Selection (AdaSS) algorithm, which trains a combined classifier based on a feature space partitioning. In this study the algorithm considers constraints put on the cost of selected features, which are one of the key-problems in the clinical decision support systems. The modified version takes into consideration both the overall classification accuracy and the cost constraints, returning balanced solution for the problem at hand. Proposed method was evaluated on the basis of computer experiments run on cost-sensitive medical benchmark datasets.