Ensemble methods for uplift modeling

Pessimistic Uplift Modeling

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the prob...

islanding detection methods for microgrids

امروزه استفاده از منابع انرژی پراکنده کاربرد وسیعی یافته است . اگر چه این منابع بسیاری از مشکلات شبکه را حل می کنند اما زیاد شدن آنها مسائل فراوانی برای سیستم قدرت به همراه دارد . استفاده از میکروشبکه راه حلی است که علاوه بر استفاده از مزایای منابع انرژی پراکنده برخی از مشکلات ایجاد شده توسط آنها را نیز منتفی می کند . همچنین میکروشبکه ها کیفیت برق و قابلیت اطمینان تامین انرژی مشترکان را افزایش ...

Uplift modeling for clinical trial data

Traditional classification methods predict the class probability distribution conditional on a set of predictor variables. Uplift modeling, in contrast, tries to predict the difference between class probabilities in the treatment group (on which some action has been taken) and the control group (not subjected to the action) such that the model predicts the net effect of the action. Such an appr...

Ensemble machine learning methods for acoustic modeling of speech

A Comparative Study of Ensemble Methods for Students Performance Modeling

Student performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate p...