Discretization Problem for Rough Sets Methods

Nguyen, Hung Son

doi:10.1007/3-540-69115-4_75

Cited by 82 publications

(38 citation statements)

References 6 publications

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“…Apart from using equivalence relations to define a rough set in complete information systems, there are also numerous studies [10,13,16,17,18] that deal with incomplete or imperfect information systems in which data are not described by precise and crisp values.…”

Section: Information Systems and Equivalence Relationmentioning

confidence: 99%

Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets

Nguyen

Ogawa

Matsumoto

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 3: Data Representation and AnalysisInternational audienceIn this research, we study several instance selection methods based on rough set theory and propose an approach able to deal with inconsistency caused by noise and imbalanced data. Recent attention has focused on the significant results obtained in selecting instances from noisy data using fuzzy-rough sets. For imbalanced data, fuzzy-rough sets approach is also applied before and after using balancing methods in order to improve classification performance. In this study, we propose an approach that uses different criteria for minority and majority classes in fuzzy-rough instance selection. It thus eliminates the step of using balancing techniques employed in controversial approach. We also carry out some experiments, measure classification performance and make comparisons with other methods

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Section: Information Systems and Equivalence Relationmentioning

confidence: 99%

Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets

Nguyen

Ogawa

Matsumoto

et al. 2015

IFIP Advances in Information and Communication Technology

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“…Therefore, the discretized decision table is irreducible. We have proposed for a discretization method that preserves some more reducts for a given decision table [5]. This method also can be solved by boolean reasoning approach, but the encoding function consists of O(mn) variables and O(n 2 2 n ) clauses.…”

Section: Discretization Problemmentioning

confidence: 99%

Approximate Boolean Reasoning Approach to Rough Sets and Data Mining

Nguyen

2005

Lecture Notes in Computer Science

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Abstract. Many problems in rough set theory have been successfully solved by boolean reasoning (BR) approach. The disadvantage of this elegant methodology is based on its high space and time complexity. In this paper we present a modified BR approach that can overcome those difficulties. This methodology is called the approximate boolean reasoning (ABR) approach. We summarize some most recent applications of ABR approach in development of new efficient algorithms in rough sets and data mining.

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“…The original framework requires that data be qualitative (discrete-valued, nominal or crisp); this means that quantitative (real-valued, continuous or fuzzy) data need to be preprocessed, either by replacing the numerical attribute values by interval codes (discretisation, see e.g. [13], [14]), or by considering a notion of approximate equality, or graded indiscernibility, between objects, leading to fuzzy-rough feature selection (FRFS) methods (see e.g. [15], [16]).…”

Section: Introductionmentioning

confidence: 99%

New Approaches to Fuzzy-Rough Feature Selection

Jensen

Shen

2009

IEEE Trans. Fuzzy Syst.

515

286

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Abstract-In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative; in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual (in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued) data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well as the traditional rough set model, as special cases.

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Discretization Problem for Rough Sets Methods

Cited by 82 publications

References 6 publications

Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets

Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets

Approximate Boolean Reasoning Approach to Rough Sets and Data Mining

New Approaches to Fuzzy-Rough Feature Selection

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