Data classification is a challenging task in era of big data due to high number of features. Feature selection is a step in process of knowledge discovery in data that aims to reduce dimensionality and improve the classification performance. The purpose of this research is to define new techniques for feature selection in order to improve classification accuracy and reduce the time required for...

This paper reports an application to educational intervention of Principal Stratification, a statistical method for estimating the effect of a treatment even when there are different rates of dropout in experimental and control conditions. We consider the potential value for using principal stratification to identify “Tough Love Interventions” – interventions that have a large effect but also i...

In direct marketing large amounts of customer data are collected that might have some complex, non linear relation to customer behavior. Data mining techniques can ooer insight in these relations. In this paper we give a basic introduction in the application of data mining to direct marketing. Best practices for data selection, algorithm selection and evaluation of results are described and ill...

The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an effective technique for dimension reduction an...

Data mining is a one of the growing sciences in the world that can play a competitive advantages rule in many firms. Data mining algorithms based on their functions can be divided in four categories; o Classification o Feature selection o Assassination rules o Clustering 03/06/2014 ITQM2014 2 DEA Entropy Method DataSets

Educational data mining (EDM) is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the behaviors of students in the learning process. In this EDM, feature selection is to be made for the generation of subset of candidate variables. As the feature selection influences the predictive accuracy o...

Educational Data Mining researchers use various prediction metrics for model selection. Often the improvements one model makes over another, while statistically reliable, seem small. The field has been lacking a metric that informs us on how much practical impact a model improvement may have on student learning efficiency and outcomes. We propose a metric that indicates how much wasted practice...

Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature se...

Feature selection is widely used in preparing highdimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social media users as well ...