General multiple-objective decision functions and linguistically quantified statements
International Journal of Man-Machine Studies - Lecture notes in computer science Vol. 174
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Improving classifier utility by altering the misclassification cost ratio
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Decision tree search methods in fuzzy modeling and classification
International Journal of Approximate Reasoning
Information Processing Letters
Small-sample precision of ROC-related estimates
Bioinformatics
Small-sample precision of ROC-related estimates
Bioinformatics
Fuzzy criteria for feature selection
Fuzzy Sets and Systems
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Artificial Intelligence in Medicine
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In many binary medical classification problems, the cost of misclassifying one category is higher than the other, and in these applications it is desirable to employ a classifier with selective sensitivity or specificity. This work explores the utility of a fuzzy multi-criteria function for performance evaluation during knowledge-based medical classification and prediction. The method presented here uses fuzzy optimization to combine the sensitivity, specificity, and accuracy of classification as goals in a single objective function. This approach is used to assign flexible goals, which can be used to maximize the outcome in terms of each one of the goals. The proposed approach significantly increases the sensitivity and the specificity while maintaining or increasing accuracy. The versatility of the method is further exploited in a multi-model approach, using individual structures of multi-objective optimization of sensitivity and specificity separately, and then combining their outcomes through a decision-making module. Among various medical benefits derived from applying this technique, the divergent feature sets selected by high sensitivity and specificity models lend insight into factors more integrally connected to what causes risk of death for patients.