LEM3 Algorithm Generalization Based on Stochastic Approximation Space

Fernández-Baizán, María C.; Pérez-Llera, C.; Feito-García, J.; Almeida, André L. F. de

doi:10.1007/3-540-45554-x_34

Cited by 2 publications

(4 citation statements)

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“…In order to learn rules from a set (whether from the set itself or from its lower or upper approximations), we have used a system for incremental learning of classification rules using examples (Fernández-Baizán et al, 2000) based on the LEM3 (Chan, 1991) and LERS (Grzymala-Busse, 1992, 1997, 1998b systems, but dealing with probabilistic approximation space (Wong and Ziarko, 1986) rather than Boolean approximation space. As described in Fernández-Baizán et al (2000) probabilistic boundaries are used to determine the lower and upper approximations of a concept, which generates certain rules with a level of certainty β (0.5 ≤ β ≤ 1).…”

Section: Decision Rulesmentioning

confidence: 99%

“…The R-lower and R-upper β approximations of X in ProbAS are defined as (Fernández-Baizán et al, 2000;Wong and Ziarko, 1986):…”

Section: Decision Rulesmentioning

confidence: 99%

“…In our approach (Fernández-Baizán et al, 2000) we have implemented the subsystem LEM2 of the system LERS (Grzymala-Busse, 1992, 1997, 1998b as the method of rules generation. This subsystem is based on learning a minimal discriminant description for each concept.…”

Section: Decision Rulesmentioning

confidence: 99%

“…In this section a rough sets analysis is obtained through three steps: variables reduction (Section 6.2), rules generation based on an extension (Fernández-Baizán et al, 2000) of algorithm LEM3 (Chan, 1991) and LEM2 (Grzymala-Busse, 1992, 1997, 1998b) (Section 6.3), and reclassification/ cross-validation tests (Section 6.4).…”

Section: Basic Characteristicsmentioning

confidence: 99%

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A model for valuation of the branch offices of a savings bank based on rough sets

Pérez-Llera

Galan

Fanjul

et al. 2004

Intelligent Systems in Accounting, Finance and Management

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This work proposes a model for the valuation of branch offices of banks based on the rough set theory, which could be used as the basis for a decision-making system for dimensioning strategies of a financial entity. It compares the rough set approach with the competitive discriminant analysis methodology using a common set of data from 421 branches. We pay special attention to data reduction and the creation of decision rules that will allow future branches to be classified. These rules could constitute the basis for the evaluation of the viability of dimensioning strategies for a financial entity. In order to evaluate the predictive capabilities of the decision rules, we present the results of cross-validation tests to evaluate the ability of the model to classify new branches. It appears that the rough sets approach provides a favourable tool for the valuation of branch offices.VALUATION BASED ON ROUGH SETS 189 Section 2 reviews rough sets theory. The relationship between rough sets theory and other classification methods is presented in Section 3. The variables of the information system are discussed in Section 4. In Section 5 a discriminant analysis of the information system formed by 421 branches is analysed. In Section 6 a rough sets analysis is obtained through three steps: variable reduction, rule generation and reclassification, and cross-validation tests. ROUGH SETS THEORY Introductory RemarksThe theory of rough sets (RS) proposed by Pawlak (1991) offers a model to deal with imprecise or incomplete information. From the start, the theory has generated great interest among researchers working in the field of automatic learning and knowledge discovery from databases (data mining). In the financial field in particular, subjects such as business failure (McKee, 2000;Slowinski et al., 1999;Zopounidis et al., 1999) and stock market analysis (Bazan et al., 1994;Grzymala-Busse, 1997;Golan, 1995) have been studied.The RS theory is used as a method to analyse data in an information system (IS). We applied this theory to the IS formed by all the branches of a Spanish savings bank. In this analysis the main problems are those related to:• The removal of superfluous variables, in order to obtain minimum subsets of variables (reducts) that assure a satisfactory approximation to the predetermined classification of branches by a decision variable. 190C. PÉREZ-LLERA ET AL.

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