Feature selection plays a critical role in biomedical data mining, driven by increasing feature
dimensionality in target problems and growing interest in advanced but computationally …

Feature selection techniques have become an apparent need in many bioinformatics
applications. In addition to the large pool of techniques that have already been developed in …

摘要分布估计算法是进化计算领域新兴起的一类随机优化算法, 是当前国际进化计算领域的研究
热点. 分布估计算法是遗传算法和统计学习的结合, 通过统计学习的手段建立解空间内个体分布 …

Traffic classification groups similar or related traffic data, which is one main stream
technique of data fusion in the field of network management and security. With the rapid …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Feature selection is an effective technique in dealing with dimensionality reduction. For
classification, it is used to find an “optimal” subset of relevant features such that the overall …

We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

DNA microarray experiments generating thousands of gene expression measurements, are
used to collect information from tissue and cell samples regarding gene expression …

Load forecasting is a crucial task, which is carried out by utility companies for sake of power
grids' successful planning, optimized operation and control, enhanced performance, and …

Feature selection plays a significant role in improving the performance of the machine
learning algorithms in terms of reducing the time to build the learning model and increasing …