Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from...

Due to the tremendous growth in e-learning in recent years, there is a need to address the issue of attrition in online courses. Predictive modeling can help identify students who may be “at-risk” to drop out from an online course. This study examines various categorical classification algorithms and evaluates the accuracy of logistic regression (LR), neural networks (Multilayer Perceptron), an...

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT ...

Latest developments in computing and technology, along with the availability of large amounts of raw data, have led to the development of many computational techniques and algorithms. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. Logis...

Now autonomous driving is a hot topic, and the identification of traffic signs also extremely important for driving. This paper mainly compares difference Support Vector Machine (SVM), Multilayer Perceptron (MLP), Logistic Regression (LR) Classifier in sign classification. The effect initial image processing on classification accuracy studied. found that sharpening significantly improved Based ...