Quick Attribute Reduction Based on Approximation Dependency Degree

Abstract

Attribute reduction is one of the core research content of Rough sets theory. Many existing algorithms mainly are aimed at the reduction of consistency decision table, and very little work has been done for attribute reduction aimed at inconsistency decision table. In fact, the methods finding Pawlak reduction from consistent decision table are not suitable for inconsistency decision table. In this paper, we introduce the approximation dependency reduction modal and present the Quick Attribution Reduction based on Approximation Dependency Degree (Quick-ARADD), which can retain the original boundary region and the original positive region unchanged, and keep the approximation accuracy unchanged for all decision equivalence classes (the partition of universe on decision attributes) of a decision table. Theoretical analysis and experimental results show that the Quick-ARADD algorithm is effective and feasible.

An Attribute Reduction Algorithm Based on Rough Set Theory and an Improved Genetic Algorithm

“…Its main idea is to use the knowledge that has been mastered to infer the fuzzy or uncertain knowledge with a certain algorithm. It has the following characteristics [2,3]: (1) When carry out data analysis with rough set theory, we just need the information provided by the data itself, without needing any extra knowledge. Although fuzzy set theory, evidence theory and probability statistics method can also be used to deal with uncertain and incomplete information, but they all need some additional knowledge, while these extra information is often uneasy to get.…”

“…Thus, the analysis of the data based on the rough set theory is more objective, simple and practical. (2) It is a powerful tool for data analysis and can be used to carry out analysis on the past data to help make a decision in the future. (3) It can be used to deal with all kinds of irregular data, including the incomplete data and uncertain data.…”

“…Rough set theory becomes an indispensable tool in the field of data mining. It develops quickly in a short time after being founded, at present, it has been used widely in the fields, such as: expert system, artificial intelligence, decision support system, knowledge discovery (data mining), pattern recognition and medical diagnostics [2], etc. It is complementary to the probability theory, the fuzzy theory, the evidence theory and other theories of processing uncertain and imprecise problems.…”

Fast assignment reduction in inconsistent incomplete decision systems

“…Many types of attribute reductions have been proposed in the area of rough sets [5][6][7][8][9][10]. These reduction algorithms can be roughly divided into two categories: exhaustive algorithms and heuristic algorithms.…”