In machine learning, discretization and feature selection (FS) are important techniques for
preprocessing data to improve the performance of an algorithm on high-dimensional data …

Feature selection aims at reducing the number of features in many applications. Existing
feature selection approaches mainly deals with classification problems with continuous or …

There is still little research work focused on feature selection (FS) techniques including both
categorical and continuous features in Software Development Effort Estimation (SDEE) …

对于混合属性条件下的特征选择问题, 给出了一种基于互信息的特征选择方法. 首先,
将互信息的定义推广到混合属性, 在给出其计算方法的基础上, 利用互信息定义了一种新的混合 …

Attribute selection procedure is a key step in the process of Knowledge Discovery in
Database (KDD). Majority of the earlier contributions of selection methods can handle easier …

Determining which genes contribute to the development of certain diseases, such as cancer,
is an important goal in the forefront of today's clinical research. This can provide insights on …

Biclustering has been recognized as a remarkably effective method for discovering local
temporal expression patterns and unraveling potential regulatory mechanisms, critical to …

Nowadays data mining algorithms are successfully applying to analyze the real data in our
life to provide useful suggestion. Since some available real data is multi-valued and multi …

In data analysis, items were mostly described by a set of characteristics called features, in
which each feature contains only single value for each object. Even so, in existence, some …

Tumor and cancer detection from microarray data are important bioinformatics problems.
These problems are quite challenging for machine learning methods, since microarray …