Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-learning via Search Combined with Parameter Optimization
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
PRIE: a system for generating rulelists to maximize ROC performance
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
Expert Systems with Applications: An International Journal
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
Hi-index | 12.05 |
The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard sate in coal mines. Due to imbalanced distribution of examples describing states ''hazardous'' and ''safe'', the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters' values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier's quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.