Selection of microbial biomarkers with genetic algorithm and principal component analysis

Learning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis

Variable Selection and Principal Component Analysis

In most of applied disciplines, many variables are sometimes measured on each individual, which result a huge data set consisting of large number of variables, say p [Sharma (1996)]. Using this collected data set in any statistical analysis may cause several troubles. The dimensionality of the data set can often be reduced, without disturbing the main features of the whole data set by Principal...

Modelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undistur...

Sparse Principal Component Analysis Incorporating Stability Selection

Principal component analysis (PCA) is a popular dimension reduction method that approximates a numerical data matrix by seeking principal components (PC), i.e. linear combinations of variables that captures maximal variance. Since each PC is a linear combination of all variables of a data set, interpretation of the PCs can be difficult, especially in high-dimensional data. In order to find ’spa...