Comparison of rough-set and statistical methods in inductive learning
International Journal of Man-Machine Studies
Instance-Based Learning Algorithms
Machine Learning
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Knowledge discovery based on neural networks
Communications of the ACM
Machine Learning
Temporal Templates and Analysis of Time Related Data
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Refining decision tree classifiers using rough set tools
International Journal of Hybrid Intelligent Systems - Hybrid Intelligence using rough sets
From Optimal Hyperplanes to Optimal Decision Trees
Fundamenta Informaticae
An application of fuzzy information granulation in the emerging area of online sports
Expert Systems with Applications: An International Journal
Induction and pruning of classification rules for prediction of microseismic hazards in coal mines
Expert Systems with Applications: An International Journal
Topological characterizations of covering for special covering-based upper approximation operators
Information Sciences: an International Journal
| Hi-index | 12.05 |
This paper presents a machine learning approach to characterizing premonitory factors of earthquake. The characteristic asymmetric distribution of seismic events and sampling limitations make it difficult to apply the conventional statistical predictive techniques. The paper shows that inductive machine learning techniques such as rough set theory and decision tree (C4.5 algorithm) allows developing knowledge representation structure of seismic activity in term of meaningful decision rules involving premonitory descriptors such as space-time distribution of radon concentration and environmental variables. The both techniques identify significant premonitory variables and rank attributes using information theoretic measures, e.g., entropy and frequency of occurrence in reducts. The cross-validation based on ''leave-one-out'' method shows that although the overall predictive and discriminatory performance of decision tree is to some extent better than rough set, the difference is not statistically significant.