Abstract-Identification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA ′ 16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average indicators evaluated by a Fisher's linear discriminant analysis. Performance evaluation experiments on the training datasets revealed an accuracy level of around 0.9438 while the performance on the test dataset was at a level of 0.9297. These results suggest that the proposed approach achieves high accuracy in predicting danger seismic events while maintaining low complexity.
Data driven marketing is becoming more and more vital for businesses day-by-day. Understanding customer behavior has the potential to decrease marketing costs as well as increase sales both in conventional marketing and online marketing. Since online users can access information faster, prices have become more competitive and customer behavior analysis has become more important. The purpose of this study is to predict the purchase interest of the users in an e-commerce web page by using the user session data such as pageview, duration etc. To this aim we used clickstream data for an e-commerce web page which is publicly available. Since only 16.5 percent of the sessions are completed with purchase in the dataset, increasing true positive rates rather than accuracy is more important. To this aim, we have explored the performance of boosting algorithms on the dataset and compared to those of state-of-the-art methods that were previously applied on the same dataset. Results show that boosting algorithms have better performance for identification of the sessions that end with a purchase.
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