An efficient Naïve Bayes classifier with negation handling for seismic hazard prediction

Abstract

Classification is the one of the most important techniques in Datamining for data analysis. In Datamining, different Classification Techniques are available to predict outcome for a given dataset. There are many classification techniques for predicting and estimating accuracy, one such famous technique is Naive Bayes Classifier. Naive Bayes is very popular as it is easy to build, not so complex and when combined with smoothing techniques give better accuracy. In this paper Naive Bayes Classifier for estimating Seismic Hazard activity is proposed. Hazard indicates a possible threat to life, health, property and environment. Mitigation of haz ard when crossing stipulated level is very important, otherwise it may lead to an emergency. One of the most dangerous hazards in mining activities is Mining Hazard. Mineral, Diamonds/Gold and Coal exploration involves mining in a big way where hazard occurrence is quite common and addressing these mining hazards is a challenging task. An important threat of Mining Hazard is Seismic Hazard which is normal in underground mines. Thus Predicting Seismic Hazard is one of the most important aspect in countering Mining Hazards. In this paper the authors are proposing a new approach to improve accuracy of predicting seismic hazard by using Naive Bayes classifier with negation handling. This approach, outperforms the traditional Naive Bayes Classifier in terms of accuracy.