Titlebook: Rough Set and Knowledge Technology; 6th International Co JingTao Yao,Sheela Ramanna,Zbigniew Suraj Conference proceedings 2011 Springer-Ver

Dawdle

Comparison of Classical Dimensionality Reduction Methods with Novel Approach Based on Formal Concepttions, lattice theory, fuzzy logic). Therefore, this method is able to bring a new insight to examined data. The comparison is accompanied by analysis of two data sets which were obtained by questionnaire survey.

BIDE

Dominance-Based Rough Set Approach on Pairwise Comparison Tables to Decision Involving Multiple Decid Rough Set Approach (DRSA), while the classical rough set approach based on equivalence relation does not consider such an order; we are taking into account multiple decision makers, while the classical rough set approach considers mostly a single classification decision provided by one decision maker only.

cravat

有特色

Dependence and Algebraic Structure of Formal Contextsn of independence of a formal context is proposed by which attribute reduction of formal context is investigated. An useful conclusion is obtained, that is, all formal contexts with common object set form a lattice.

下級(jí)

An Efficient Fuzzy-Rough Attribute Reduction Approacha strategy to improve a heuristic process of fuzzy-rough feature selection. Experiments show that this modified algorithm is much faster than its original version. It is worth noting that the performance of the modified algorithm becomes more visible when dealing with larger data sets.

Curmudgeon

OPINE

thrombosis

Conference proceedings 2011and selected from 229 submissions. The papers are organized in topical sections on attribute reduction and feature selection, generalized rough set models, machine learning with rough and hybrid techniques, knowledge technology and intelligent systems and applications.

Invigorate

Mining Incomplete Data—A Rough Set Approacheralization of the elementary set well-known in rough set theory, may be computed using such blocks. For incomplete data sets three different types of global approximations: singleton, subset and concept are defined. Additionally, for incomplete data sets a local approximation is defined as well.

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