Sampling-based sequential subgroup mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On the Tractability of Rule Discovery from Distributed Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
New probabilistic interest measures for association rules
Intelligent Data Analysis
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Measures of Ruleset Quality Capable to Represent Uncertain Validity
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
DS '08 Proceedings of the 11th International Conference on Discovery Science
Implementing a Rule Generation Method Based on Secondary Differences of Two Criteria
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2005 conference on Multi-Relational Data Mining
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Evaluation Measures for Multi-class Subgroup Discovery
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
ROCCER: an algorithm for rule learning based on ROC analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Evaluation of a Classification Rule Mining Algorithm Based on Secondary Differences
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules
DS '09 Proceedings of the 12th International Conference on Discovery Science
Rule extraction from support vector machines: A review
Neurocomputing
Approximate Bayesian inference in spatial GLMM with skew normal latent variables
Computational Statistics & Data Analysis
Induction and pruning of classification rules for prediction of microseismic hazards in coal mines
Expert Systems with Applications: An International Journal
Secure top-k subgroup discovery
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Itemset mining: A constraint programming perspective
Artificial Intelligence
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Tight combinatorial generalization bounds for threshold conjunction rules
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
International Journal of Applied Mathematics and Computer Science
The study of the solvability of the genome annotation problem on sets of elementary motifs
Pattern Recognition and Image Analysis
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Why is rule learning optimistic and how to correct it
ECML'06 Proceedings of the 17th European conference on Machine Learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Reinventing machine learning with ROC analysis
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Optimized rule mining through a unified framework for interestingness measures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
From local to global patterns: evaluation issues in rule learning algorithms
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Local bayesian based rejection method for HSC ensemble
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Multi-class correlated pattern mining
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Secure Distributed Subgroup Discovery in Horizontally Partitioned Data
Transactions on Data Privacy
Heuristic rule-based regression via dynamic reduction to classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Dynamic Programming Approach for Partial Decision Rule Optimization
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Dynamic programming approach to optimization of approximate decision rules
Information Sciences: an International Journal
Improving the cAnt-MinerPB classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Minors as miners: modelling and evaluating ontological and linguistic learning
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Evaluating the use of different measure functions in the predictive quality of ABC-miner
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
MiningZinc: a modeling language for constraint-based mining
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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This paper provides an analysis of the behavior of separate-and-conquer or covering rule learning algorithms by visualizing their evaluation metrics and their dynamics in coverage space, a variant of ROC space. Our results show that most commonly used metrics, including accuracy, weighted relative accuracy, entropy, and Gini index, are equivalent to one of two fundamental prototypes: precision, which tries to optimize the area under the ROC curve for unknown costs, and a cost-weighted difference between covered positive and negative examples, which tries to find the optimal point under known or assumed costs. We also show that a straightforward generalization of the m-estimate trades off these two prototypes. Furthermore, our results show that stopping and filtering criteria like CN2's significance test focus on identifying significant deviations from random classification, which does not necessarily avoid overfitting. We also identify a problem with Foil's MDL-based encoding length restriction, which proves to be largely equivalent to a variable threshold on the recall of the rule. In general, we interpret these results as evidence that, contrary to common conception, pre-pruning heuristics are not very well understood and deserve more investigation.