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On the influence of dependent features in classification problems: A game-theoretic perspective

EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Davila-Pena, Laura Saavedra-Nieves, Alejandro Casas-Mendez, Balbina Univ Kent Ctr Logist & Sustainabil Analyt CeLSA Kent Business Sch Dept Analyt Operat & Syst Canterbury CT2 7PE Kent England Univ Santiago de Compostela Fac Math Dept Stat Math Anal & Optimisat MODESTYA Res Grp Campus Vida Santiago De Compostela 15782 Spain Univ Santiago de Compostela Fac Math Dept Stat Math Anal & Optimisat MODESTYA Res GrpCITMAga Campus Vida Santiago De Compostela 15782 Spain

This paper deals with a new measure of the influence of each feature on the outcome in classification problems, accounting for potential dependencies among certain feature subsets. Within this framework, we consider a sample of individuals characterized by specific features, each feature encompassing a finite range of values, and classified based on a binary outcome. This measure turns out to be an influence measure explored in existing literature and related to cooperative game theory. We provide an axiomatic characterization of our proposed influence measure by tailoring properties from the cooperative game theory to our specific context. Furthermore, we demonstrate that our influence measure becomes a general characterization of the wellknown Banzhaf-Owen value for games with a priori unions, from the perspective of classification problems. The definitions and results presented herein are illustrated through numerical examples and various applications, offering practical insights into our methodologies.

关键词： classification problems Influence measure of features Dependent features Axiomatic characterization Banzhaf-Owen value

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Characterisation of environment type and difficulty for streamed data classification problems

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INFORMATION SCIENCES 2021年 569卷 615-649页

作者： Ellis, Mathys Bosman, Anna S. Engelbrecht, Andries P. Univ Pretoria Dept Comp Sci Pretoria South Africa Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Comp Sci Div Stellenbosch South Africa

SDCP require classifiers with the ability to learn and to adjust to the underlying relationships in data streams in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to find the optimal decision boundaries, but also to track changes in the decision boundaries as new training data is received. Each SDCP can be described in terms of its environment and difficulty. The environment of an SDCP describes the rate and magnitude of changes in the decision boundaries in the data streams. On the other hand, the difficulty of an SDCP describes the availability of the data that define the decision boundaries during an environment instance. In any empirical analysis of streamed data classifiers, a set of SDCP is used. Understanding the environment and difficulty of each SDCP allows for a more holistic analysis of empirical results. This article proposes (i) a novel quantitative method for analysing the environment of SDCP, and (ii) a difficulty classification scheme based on the construction of SDCP. The proposed methods are evaluated by applying them to a benchmark suite of SDCP. (c) 2021 Elsevier Inc. All rights reserved. Data streams are common sources of data in the real world [1]. Because data streams provide sequential access to real-time data, the data they provide is referred to as streamed data [22,1]. The task of classifying streamed data according to a set of known labels in real-time is known as the streamed data classification problem (SDCP) [10]. Streamed data can undergo changes due to their real-world sources [38]. These changes may cause the decision boundaries of SDCPs to either disappear, appear, or shift [37]. If a classifier is unable to track decision boundary changes, then the classifier will experience concept drift [38]. That is, the classifier will not be able to classify correctly due to the changed decision boundaries [47]. Examples of SDCPs include price trend analysis of a financial market pric

关键词： Data streams classification problems Streamed data classification problems Dynamic environment analysis Problem difficulty analysis

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A Hybrid Higher Order Neural Classifier for handling classification problems

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第1期38卷 386-393页

作者： Fallahnezhad, Mehdi Moradi, Mohammad Hassan Zaferanlouei, Salman Amirkabir Univ Technol Fac Biomed Engn Ctr Excellence Biomed Engn Lab Biomed Signal ProcBioelect Dept Tehran Iran Amirkabir Univ Technol Fac Nucl Engn Nucl Engn & Phys Dept Tehran Iran

In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of different high-order units and updating the learning parameters. Structure, model selection and updating the learning parameters of HHONC is introduced and is applied in classification of the Iris data set, the breast cancer data set, the Wine recognition data set, the Glass identification data set, the Balance scale data set, and the Pima diabetes data set. Acquired results are compared with the methods presented in Chen and Shie (2009). It is observed that the fewer features the dataset contains, the more accurate the HHONC performs, however the accuracy of datasets with more features are acceptable. Experimental results show about 3.5% and 0.6% improvements compared to the best accuracy obtained in previously methods for classifying the Pima diabetes and Iris datasets, respectively. In addition, by using a same method for reducing the feature number, it's shown the proposed method perform more accurate than methods presented in Shie and Chen (2008). In this case, improvements compared to the best acquired accuracy of mentioned methods are about 1.7%, 1.3% and 0.2% in classification of Pima, Iris and Breast cancer datasets, respectively. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Higher order neural network (HONN) classification problems High-order unit Model selection Feature subset selection

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Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 1005-1016页

作者： Gopalakrishna, Aravind Kota Ozcelebi, Tanir Lukkien, Johan J. Liotta, Antonio Eindhoven Univ Technol Dept Math & Comp Sci Syst Architecture & Networking Grp Eindhoven Netherlands Univ Derby Data Sci Res Ctr Derby England

Cognitive applications that involve complex decision making such as smart lighting have non-deterministic input-output relationships, i.e., more than one output may be acceptable for a given input. We refer them as non-deterministic multiple output classification (nDMOC) problems, which are particularly difficult for machine learning (ML) algorithms to predict outcomes accurately. Evaluating ML algorithms based on commonly used metrics such as classification Accuracy (CA) is not appropriate. In a batch setting, Relevance Score (RS) was proposed as a better alternative, which determines how relevant a predicted output is to a given context. We introduce two variants of RS to evaluate ML algorithms in an online setting. Furthermore, we evaluate the algorithms using different metrics for two datasets that have non-deterministic input-output relationships. We show that instance-based learning provides superior RS performance and the RS performance keeps improving with an increase in the number of observed samples, even after the CA performance has converged to its maximum. This is a crucial result as it illustrates that RS is able to capture the performance of ML algorithms in the context of nDMOC problems while CA cannot. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cognitive systems Machine learning classification problems Performance metric Relevance score Non-deterministic multiple output classification Human factors Smart lighting

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Application of the method of maximum entropy in the mean to classification problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 437卷 101-108页

作者： Gzyl, Henryk ter Horst, Enrique Molina, German IESA Ctr Finanzas Caracas Venezuela CESA Bogota Colombia Idal Capital Grp Los Angeles CA USA

In this note we propose an application of the method of maximum entropy in the mean to solve a class of inverse problems comprising classification problems and feasibility problems appearing in optimization. Such problems may be thought of as linear inverse problems with convex constraints imposed on the solution as well as on the data. The method of maximum entropy in the mean proves to be a very useful tool to deal with this type of problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Maximum entropy in the mean classification problems Credit scoring Linear inverse problems

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Empirical study of feature selection methods based on individual feature evaluation for classification problems

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8170-8177页

作者： Arauzo-Azofra, Antonio Aznarte, Jose Luis Benitez, Jose M. Univ Cordoba Area Project Engn E-14071 Cordoba Spain Ecole Mines Paris Ctr Energy & Proc Sophia Antipolis France Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different selection criteria and individual feature evaluation modules. These methods are commonly used because of their low running time. After carrying out a thorough empirical study the most interesting methods are identified and some recommendations about which feature selection method should be used under different conditions are provided. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Feature selection Feature evaluation classification problems Data reduction Feature estimation

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Belief rule based expert system for classification problems with new rule activation and weight calculation procedures

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INFORMATION SCIENCES 2016年 336卷 75-91页

作者： Chang, Leilei Zhou, ZhiJie You, Yuan Yang, Longhao Zhou, Zhiguo High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Fuzhou Univ Decis Sci Inst Fuzhou 350116 Peoples R China Univ Texas SW Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA

classification problems are significant because they constitute a meta-model for multiple theoretical and practical applications from a wide range of fields. The belief rule based (BRB) expert system has shown potentials in dealing with both quantitative and qualitative information under uncertainty. In this study, a BRB classifier is proposed to solve the classification problem. However, two challenges must be addressed. First, the size of the BRB classifier must be controlled within a feasible range for better expert involvement. Second, the initial parameters of the BRB classifier must be optimized by learning from the experts' knowledge and/or historic data. Therefore, new rule activation and weight calculation procedures are proposed to downsize the BRB classifier while maintaining the matching degree calculation procedure. Moreover, the optimal algorithm using the evidential reasoning (ER) algorithm as the inference engine and the differential evolution (DE) algorithm as the optimization engine is proposed to identify the fittest parameters, including the referenced values of the antecedent attributes, the weights of the rules and the beliefs of the degrees in the conclusion. Five benchmarks, namely, iris, wine, glass, cancer and pima, are studied to validate the efficiency of the proposed BRB classifier. The result shows that all five benchmarks could be precisely modeled with a limited number of rules. The proposed BRB classifier has also shown superior performance in comparing it with the results in the literature. (C) 2015 Elsevier Inc. All rights reserved.

关键词： classification problems Belief rule base Optimization algorithm

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AN ALGORITHM TO GENERATE RADIAL BASIS FUNCTION (RBF)-LIKE NETS FOR classification problems

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NEURAL NETWORKS 1995年第2期8卷 179-201页

作者： ROY, A GOVIL, S MIRANDA, R Arizona State University USA

This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems. The method uses linear programming (LP) models to train the RBF-like net. Polynomial time complexity of the method is proven and computational results are provided for many, well-known problems. The method can also be implemented as an on-line adaptive algorithm.

关键词： RADIAL BASIS FUNCTION-LIKE NETS classification problems LINEAR PROGRAMMING MODELS

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Mining fuzzy association rules for classification problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2002年第4期43卷 735-750页

作者： Hu, YC Chen, RS Tzeng, GH Natl Chiao Tung Univ Inst Management Technol Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Informat Management Hsinchu 300 Taiwan

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules;therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： data mining knowledge acquisition classification problems association rules

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Determining membership functions and minimum fuzzy support in finding fuzzy association rules for classification problems

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KNOWLEDGE-BASED SYSTEMS 2006年第1期19卷 57-66页

作者： Hu, YC Chung Yuan Christian Univ Dept Business Adm Chungli 320 Taiwan

This paper propose a new method, that employs the genetic algorithm, to find fuzzy association rules for classification problems based on an effective method for discovering the fuzzy association rules, namely the fuzzy grids based rules mining algorithm (FGBRMA). It is considered that some important parameters, including the number and shapes of membership functions in each quantitative attribute and the minimum fuzzy support, are not easily user-specified. Thus, the above-mentioned parameters are automatically determined by a binary string or chromosome is composed of two substrings: one for each quantitative attribute by the coding method proposed by Ishibuchi and Murata, and the other for the minimum fuzzy support. In each generation, the fitness value, which maximizes the classification accuracy rate and minimizes the number of fuzzy rules, of each chromosome can be obtained. When reaching the termination condition, a chromosome with maximum fitness value is then used to test its performance. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that proposed method performs well in comparison with other classification methods. (c) 2005 Elsevier B.V. All rights reserved.

关键词： data mining knowledge acquisition classification problems fuzzy association rules genetic algorithms

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