Propositionalization-based relational subgroup discovery with RSD
Machine Learning
On handling conflicts between rules with numerical features
Proceedings of the 2006 ACM symposium on Applied computing
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
Classifying Chemical Compounds Using Contrast and Common Patterns
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
An Improved Model Selection Heuristic for AUC
ECML '07 Proceedings of the 18th European conference on Machine Learning
Developing a bioaerosol detector using hybrid genetic fuzzy systems
Engineering Applications of Artificial Intelligence
An experimental comparison of performance measures for classification
Pattern Recognition Letters
A symbolic fault-prediction model based on multiobjective particle swarm optimization
Journal of Systems and Software
Exploring multi-objective PSO and GRASP-PR for rule induction
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Genetic rule extraction optimizing brier score
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Defect prediction from static code features: current results, limitations, new approaches
Automated Software Engineering
Information and Software Technology
Using OVA modeling to improve classification performance for large datasets
Expert Systems with Applications: An International Journal
Local patterns: theory and practice of constraint-based relational subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Integrating binding site predictions using non-linear classification methods
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Oracle coached decision trees and lists
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Performance of classification models from a user perspective
Decision Support Systems
The Journal of Machine Learning Research
Positive-versus-Negative Classification for Model Aggregation in Predictive Data Mining
INFORMS Journal on Computing
IIvotes ensemble for imbalanced data
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
| Hi-index | 0.00 |
Rules are commonly use for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce categorical classifications to maximize classification accuracy. Recent work in machine learning has pointed out the limitations of classification accuracy: when class distributions are skewed, or error costs are unequal, an accuracy maximizing rule set can perform poorly. Amore flexible use of a rule set is to produce instance scores indicating the likelihood that an instance belongs to a given class. With such an ability, we can apply rulesets effectively whendistributions are skewed or error costs are unequal. This paper empirically investigates different strategies for evaluating rule sets when the goal is to maximize the scoring (ROC)performance.