We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while i...

introduction the increasing use of satellite remote sensing data for civilian use has proved to be the most cost-effective means of mapping and monitoring for environmental changes. satellite remote sensing has played a pivotal role in finding forest cover, vegetation type and land use changes in urban areas. one of the most complete of these methods is classification. the conventional per-pixe...

Propensity score weighting is sensitive to model misspecification and outlying weights that can unduly influence results. The authors investigated whether trimming large weights downward can improve the performance of propensity score weighting and whether the benefits of trimming differ by propensity score estimation method. In a simulation study, the authors examined the performance of weight...

Customer Relationship Management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers are significant, an increase of the retention rate can be very profitable. In this paper, we focus on the treatment of a company’s most promising customers in a non-contractual setting. We build a model in order to...

In this paper, we propose a novel data mining technique involving random forests and random trees for energy efficiency for forest cover type classification. Novel machine learning and data mining techniques provide an unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks. However, given the sheer amount o...