11,026 research outputs found
Knowledge reduction of dynamic covering decision information systems with varying attribute values
Knowledge reduction of dynamic covering information systems involves with the
time in practical situations. In this paper, we provide incremental approaches
to computing the type-1 and type-2 characteristic matrices of dynamic coverings
because of varying attribute values. Then we present incremental algorithms of
constructing the second and sixth approximations of sets by using
characteristic matrices. We employ experimental results to illustrate that the
incremental approaches are effective to calculate approximations of sets in
dynamic covering information systems. Finally, we perform knowledge reduction
of dynamic covering information systems with the incremental approaches
Preprocessing and Feature Selection on Group Structure Analysis using Entropy and Thresholding
Many real data increase dynamically in size. We have been observing in many fields that data grow with time in size. This has led to the development of several new analytic techniques. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy.When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient
Fuzzy-rough set models and fuzzy-rough data reduction
Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduced as a fuzzy generalization of rough sets. This paper reviewed the most important contributions to the rough set theory, fuzzy rough set theory and their applications. In many real world situations, some of the attribute values for an object may be in the set-valued form. In this paper, to handle this problem, we present a more general approach to the fuzzification of rough sets. Specially, we define a broad family of fuzzy rough sets. This paper presents a new development for the rough set theory by incorporating the classical rough set theory and the interval-valued fuzzy sets. The proposed methods are illustrated by an numerical example on the real case
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