FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute

Kryszkiewicz, Marzena; Lasek, Piotr

doi:10.1007/978-3-540-89876-4_5

Cited by 19 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A strong connection between the notions of a reduct RED(S) in an information system S and prime implicant of the monotonic boolean function f S was investigated among others by Skowron, Kryszkiewicz and Słowiński [9], [13]:…”

Section: Elimination Of Input Variablesmentioning

confidence: 99%

Features Reduction Using Logic Minimization Techniques

Borowik¹,

Łuba²,

Zydek³

2012

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Abstract-This paper is dedicated to two seemingly different problems. The first one concerns information theory and the second one is connected to logic synthesis methods. The reason why these issues are considered together is the important task of the efficient representation of data in information systems and as well as in logic systems. An efficient algorithm to solve the task of attributes/arguments reduction is presented.

show abstract

Section: Elimination Of Input Variablesmentioning

confidence: 99%

Features Reduction Using Logic Minimization Techniques

Borowik¹,

Łuba²,

Zydek³

2012

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

show abstract

“…Attribute reduction does not attempt to maximize the class separability but rather to keep the discernibility ability of the original ones [8,11,12,26,31,37,42]. In the last two decades, researchers have proposed many reduction algorithms [10,14,20,21,29,39,40]. However, most of these algorithms can only be applicable to static data sets.…”

Section: Introductionmentioning

confidence: 98%

“…These techniques usually can directly carry out the computation using the existing result from the original data set [9,14,18,22,36]. A common character of these algorithms is that they were proposed to deal with dynamically-increasing data sets in an incremental manner.…”

Section: Introductionmentioning

confidence: 99%

Attribute reduction: A dimension incremental strategy

Wang

Liang

Qian

2013

Knowledge-Based Systems

106

View full text Add to dashboard Cite

“…Applying discernibility matrix, Skowron [42] proposed an attribute reduction algorithm by computing disjunctive normal form, which is able to obtain all attribute reducts of a given table whereas finding the minimal reduct of a decision table is an NP-hard problem. Kryszkiewicz and Lasek [22] proposed an approach to computing the minimal set of attributes that functionally determine a decision attribute. These two attribute reduction algorithms are usually computationally very expensive, especially for dealing with large-scale data sets of high dimensions.…”

Section: Introductionmentioning

confidence: 99%

An efficient rough feature selection algorithm with a multi-granulation view

Liang

Wang

Dang

et al. 2012

International Journal of Approximate Reasoning

162

View full text Add to dashboard Cite

Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets.

show abstract

FUN: Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute

Cited by 19 publications

References 15 publications

Features Reduction Using Logic Minimization Techniques

Features Reduction Using Logic Minimization Techniques

Attribute reduction: A dimension incremental strategy

An efficient rough feature selection algorithm with a multi-granulation view

Contact Info

Product

Resources

About