Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer.

Gene Classification Based on Multi-Class SVMs with Systematic Sampling and Hierarchical Clustering (SSHC) Algorithm.

The support vector machines (SVMs) is one of the machine learning algorithms with high classification accuracy. However, the support vector machine algorithm has a very high training complexity. Thus, it is not very efficient with large datasets.

Digging for Significant Genes in Microarray Expression Data Based on Systematic Sampling and Hierarchal Clustering Algorithm.

Obesity is a worldwide health problem. Eating habits have changed during this decade and an increase in high-fat foods as well as sugar intake has been observed, which is associated with obesity and weight gaining. Therefore, in this chapter, we have analysed microarray expression data for obese and lean individuals.