Predicting the cost of development effort is essential for successful projects. This helps software project managers to allocate resources, and determine budget or delivery date. paper evaluates a set machine learning algorithms techniques in predicting A feature selection algorithm utilized enhance accuracy prediction process. evaluations are presented based on basic classifiers stacked ensemb...

Since accurate classification of DNA microarray is a very important issue for the treatment of cancer, it is more desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of the many advantages of mutually error-correlated ensemble classifiers, they are limited in performance. It is difficult to c...

In machine learning systems, especially in medical applications, clinical datasets usually contain high dimensional feature spaces with relatively few samples that lead to poor classifier performance. To overcome this problem, feature selection and ensemble classification are applied in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelev...

Currently, a lot of research in the field of intelligent tutoring systems is concerned with recognising student’s emotions and affects. The recognition is done by extracting features from information sources like speech, typing and mouse clicking behaviour or physiological sensors. Multimodal affect recognition approaches use several information sources. Those approaches usually focus on the re...

Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, the same domains it much more relevant to correctly classify and profile minority class observations. This need can be addressed by feature selection (FS), that offers several further advantages, such as decreasing computational costs, aiding inference interpretability. However, traditional FS tech...