A comparison of fuzzy strategies for corporate acquisition analysis

Glackin, Cornelius; Maguire, Liam; McIvor, Ronan; Humphreys, Paul; Herman, Pawel

doi:10.1016/j.fss.2007.03.020

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…Previous works have already employed type-2 fuzzy classifiers in financial domain [15]- [17] and have shown how these systems outperform their type-1 versions and other state of the art classifiers. However, to date most of the type-2 fuzzy systems reported in the literature have been generated from data by optimizing only the accuracy, while neglecting the complexity [17]- [23].…”

Section: [1]-[3])mentioning

confidence: 99%

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Antonelli

Bernardo

Hagras

et al. 2017

IEEE Trans. Fuzzy Syst.

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Abstract-Classification techniques are becoming essential in the financial world for reducing risks and possible disasters. Managers are interested in not only high accuracy but also in interpretability and transparency. It is widely accepted now that the comprehension of how inputs and output are related to each other is crucial for taking operative and strategic decisions. Furthermore, inputs are often affected by contextual factors and characterized by a high level of uncertainty. In addition, financial data are usually highly skewed towards the majority class. With the aim of achieving high accuracies, preserving the interpretability and managing uncertain and unbalanced data, the paper presents a novel method to deal with financial data classification by adopting type-2 fuzzy rule-based classifiers (FRBCs) generated from data by a multi-objective evolutionary algorithm (MOEA). The classifiers employ an approach, denoted as scaled dominance, for defining rule weights in such a way to help minority classes to be correctly classified. In particular, we have extended PAES-RCS, an MOEA-based approach to learn concurrently the rule and data bases of FRBCs, for managing both interval type-2 fuzzy sets and unbalanced datasets. To the best of our knowledge, this is the first work that generates type-2 FRBCs by concurrently maximizing accuracy and minimizing the number of rules and the rule length with the objective of producing interpretable models of real-world skewed and incomplete financial datasets. The rule bases are generated by exploiting a rule and condition selection (RCS) approach, which selects a reduced number of rules from a heuristically generated rule base and a reduced number of conditions for each selected rule during the evolutionary process. The weight associated with each rule is scaled by the scaled dominance approach on the fuzzy frequency of the output class, in order to give a higher weight to the minority class. As regards the data base learning, the membership function parameters of the interval type-2 fuzzy sets used in the rules are learned concurrently to the application of RCS. Unbalanced datasets are managed by using, in addition to complexity, selectivity and specificity as objectives of the MOEA rather than only the classification rate. We tested our approach, named IT2-PAES-RCS, on eleven financial datasets and compared our results with the ones obtained by the original PAES-RCS with three objectives and with and without scaled dominance, the fuzzy rule-based classifiers FARC-HD and FURIA, the classical C4.5 decision tree algorithm and its costsensitive version. Using non-parametric statistical tests, we will show that IT2-PAES-RCS generates FRBCs with, on average, accuracy statistically comparable to and complexity lower than the ones generated by the two versions of the original PAES-RCS. Further, the FRBCs generated by FARC-HD and FURIA and the decision trees computed by C4.5 and its cost-sensitive version, despite the highest complexity, result to be less accurate than the FRBCs...

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Section: [1]-[3])mentioning

confidence: 99%

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Antonelli

Bernardo

Hagras

et al. 2017

IEEE Trans. Fuzzy Syst.

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“…In the stock portfolio selection system proposed in this study, the GRA method is used to simplify the stock classification and selection processes by consolidating the values of the multiple attributes of each data object into a limited number of attribute values, each representing one particular sub-system of the total stock system. [38,39] The fuzzy C-means (FCM) clustering method, developed by Dunn in 1973 [40] and later refined by Bezdek [27], has many applications, ranging from feature analysis to clustering and classifier design. The FCM clustering method consists of two basic procedures, namely (1) calculating the cluster centroids within the dataset, and (2) determining the cluster memberships of each data object.…”

Section: Grey Relational Analysismentioning

confidence: 99%

Application of enhanced cluster validity index function to automatic stock portfolio selection system

Huang

Wan

2011

Inf Technol Manag

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This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.

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“…Review of related methodologies FCM clustering method (Glackina et al, 2007) FCM, first developed by Dunn in 1973(Dunn, 1973 and later refined by Bezdek in 1981(Bezdek, 1981, is an unsupervised clustering algorithm with multiple applications, ranging from feature analysis, to clustering and classifier design. Fuzzy clustering techniques differ from hard clustering algorithms such as the K-means scheme in that a single data object may be mapped simultaneously to multiple clusters rather than being assigned exclusively to a single cluster.…”

Section: Introductionmentioning

confidence: 99%

A classification approach based on variable precision rough sets and cluster validity index function

Lin

2014

Engineering Computations

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Purpose – The clustering/classification method proposed in this study, designated as the PFV-index method, provides the means to solve the following problems for a data set characterized by imprecision and uncertainty: first, discretizing the continuous values of all the individual attributes within a data set; second, evaluating the optimality of the discretization results; third, determining the optimal number of clusters per attribute; and fourth, improving the classification accuracy (CA) of data sets characterized by uncertainty. The paper aims to discuss these issues. Design/methodology/approach – The proposed method for the solution of the clustering/classifying problem, designated as PFV-index method, combines a particle swarm optimization algorithm, fuzzy C-means method, variable precision rough sets theory, and a new cluster validity index function. Findings – This method could cluster the values of the individual attributes within the data set and achieves both the optimal number of clusters and the optimal CA. Originality/value – The validity of the proposed approach is investigated by comparing the classification results obtained for UCI data sets with those obtained by supervised classification BPNN, decision-tree methods.

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A comparison of fuzzy strategies for corporate acquisition analysis

Cited by 19 publications

References 35 publications

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Application of enhanced cluster validity index function to automatic stock portfolio selection system

A classification approach based on variable precision rough sets and cluster validity index function

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