Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

Abstract

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.