The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella,...

The problem: Much of finance theory is based on the efficient market hypothesis. According to this hypothesis, the prices of financial assets, such as stocks, incorporate all information that may affect their future performance. However, the translation of publicly available information into...

With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine...

Data mining is an approach to discover knowledge from large data. Pollutant forecasting is an important problem in the environmental sciences. This paper tries to use data mining methods to forecast fine particles (PM2.5) concentration level in a new town of Hong Kong rural area. There are...

Most induction algorithms for building predictive models take as input training data in the form of feature vectors. Acquiring the values of features may be costly, and simply acquiring all values may be wasteful or prohibitively expensive. Active feature-value acquisition (AFA) selects features...

Logistic regression (LR) continues to be one of the most widely used methods in data mining in general and binary data classification in particular. This paper is focused on providing an overview of the most important aspects of LR when used in data analysis, specifically from an algorithmic and...

Knowledge, as intellectual capital, has become the main resource of an organization, and the process of knowledge discovery, acquisition and storage is a very important one. Knowledge discovery can be easily realized through Data Mining, a Machine Learning technique, which allows the discovery...

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction....

Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes...