Principal Component Analysis

Statistical analysis of three new measures of relevance redundancy and complementarity

Discriminant analysis is part of statistical learning; its goal is to separate classes defined a priori on a population and involves predicting the class of given data points.  Discriminant analysis is applied in various fields such as pattern recognition, DNA microarray etc.  In recent years, the discrimination problem remains a challenging task that has received increasing attention, especially for high-dimensional data sets.  Indeed, in such a case, the feature selection is necessary, which implies the use of criteria of relevance, redundancy and complementarity of e

Use of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries

Prevalence of diabetes in Gulf countries is knowing a significant increase because of various risk factors, such as: obesity, unhealthy diet, physical inactivity and smoking.  The aim of our proposed study is to use Data Mining and Data Analysis tools in order to determine different risk factors of the development of Type 2 diabetes mellitus (T2DM) in Gulf countries, from Gulf COAST dataset.

Hybrid swarm negative selection algorithm for dna-microarray data classification

In the paper, a classification method is proposed. It is based on Combined Swarm Negative Selection Algorithm, which was originally designed for binary classification problems. The accuracy of developed algorithm was tested in an experimental way with the use of microarray data sets. The experiments confirmed that direction of changes introduced in developed algorithm improves its accuracy in comparison to other classification algorithms.