The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

对于空间中的任一子集, 通过基本邻域信息粒子进行逼近, 由此提出了邻域信息系统和邻域决策
表模型. 分析了该模型的性质, 并且基于此模型构造了数值型属性的选择算法. 利用UCI …

Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition
and machine learning, however, as a similar lazy classifier using local information for …

Rough set theory can be applied to rule induction. There are two different types of
classification rules, positive and boundary rules, leading to different decisions and …

Feature subset selection presents a common challenge for the applications where data with
tens or hundreds of features are available. Existing feature selection algorithms are mainly …

In rough set theory, the lower and upper approximation operators defined by binary relations
satisfy many interesting properties. Various generalizations of Pawlak's rough …

Attribute reduction is a basic issue in knowledge representation and data mining. This paper
deals with attribute reduction in incomplete information systems and incomplete decision …

Attribute reduction is considered as an important preprocessing step for pattern recognition,
machine learning, and data mining. This paper provides a systematic study on attribute …

Generally speaking, there are four fuzzy approximation operators defined on a general
triangular norm (t-norm) framework in fuzzy rough sets. Different types of t-norms specify …