Comparison of fusion methods from the abstract level and the rank level in a dispersed decision-making system

Issues related to decision making based on dispersed knowledge are discussed in the paper. A dispersed decision-making system, which was proposed by the authors in previous articles, is used in this paper. In the system, a process of combining classifiers into coalitions with a negotiation stage is realized. The novelty that is proposed in this article involves the use of six different methods of conflict analysis that are known from the literature.The main purpose of the tests, which were performed, was to compare the methods from the two groups – the abstract level and the rank level. An additional aim was to investigate the efficiency of the fusion methods used in a dispersed system with a dynamic structure with the efficiency that is obtained when no structure is used. Conclusions were drawn that, in most cases, the use of a dispersed system improves the efficiency of inference.

[1]  Andrzej Skowron,et al.  Wisdom Technology: A Rough-Granular Approach , 2009, Aspects of Natural Language Processing.

[2]  Xin Yao,et al.  An analysis of diversity measures , 2006, Machine Learning.

[3]  Jianping Gou,et al.  A Novel Weighted Voting for K-Nearest Neighbor Rule , 2011, J. Comput..

[4]  Alicja Wakulicz-Deja,et al.  Application of Reduction of the Set of Conditional Attributes in the Process of Global Decision-making , 2013, Fundam. Informaticae.

[5]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[7]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[8]  Harris Drucker,et al.  Boosting and Other Ensemble Methods , 1994, Neural Computation.

[9]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[10]  Kenneth W. Bauer,et al.  An investigation of the effects of correlation and autocorrelation on classifier fusion and optimal classifier ensembles , 2008, Int. J. Gen. Syst..

[11]  Alicja Wakulicz-Deja,et al.  Global decision-making in multi-agent decision-making system with dynamically generated disjoint clusters , 2016, Appl. Soft Comput..

[12]  Steven J. Simske,et al.  Performance analysis of pattern classifier combination by plurality voting , 2003, Pattern Recognit. Lett..

[13]  Gregory Levitin,et al.  Reliability optimization for weighted voting system , 2001, Reliab. Eng. Syst. Saf..

[14]  Sebastian Widz,et al.  Rough Set Based Decision Support—Models Easy to Interpret , 2012 .

[15]  Alicja Wakulicz-Deja,et al.  A dispersed decision-making system - The use of negotiations during the dynamic generation of a system's structure , 2014, Inf. Sci..

[16]  Tenne Yoel,et al.  An algorithm for computationally expensive engineering optimization problems , 2013 .

[17]  Sinh Hoa Nguyen,et al.  Rough Set Approach to Sunspot Classification Problem , 2005, RSFDGrC.

[18]  Kevin W. Bowyer,et al.  Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Marina L. Gavrilova,et al.  Multimodal Biometric System Using Rank-Level Fusion Approach , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Xin Yao,et al.  Multi-network evolutionary systems and automatic decomposition of complex problems , 2006, Int. J. Gen. Syst..

[21]  Boris G. Mirkin,et al.  Choosing a discernibility measure for reject-option of individual and multiple classifiers , 2010, Int. J. Gen. Syst..

[22]  Jonathan M. Garibaldi,et al.  A 'non-parametric' version of the naive Bayes classifier , 2011, Knowl. Based Syst..

[23]  L. Shapley,et al.  Optimizing group judgmental accuracy in the presence of interdependencies , 1984 .

[24]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[25]  Lawrence O. Hall,et al.  Using classifier ensembles to label spatially disjoint data , 2008, Inf. Fusion.

[26]  Alicja Wakulicz-Deja,et al.  Application of the Method of Editing and Condensing in the Process of Global Decision-making , 2011, Fundam. Informaticae.

[27]  Te-Wei Chiang,et al.  Combination of Multiple Classifiers for , 2004 .

[28]  Dominik Slezak,et al.  Ensembles of Bireducts: Towards Robust Classification and Simple Representation , 2011, FGIT.

[29]  Alicja Wakulicz-Deja,et al.  The strength of coalition in a dispersed decision support system with negotiations , 2016, Eur. J. Oper. Res..

[30]  Isabelle Bloch,et al.  Fuzzy relative position between objects in images: a morphological approach , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[31]  Keung-Chi Ng,et al.  Probabilistic multi-knowledge-base systems , 1994, Applied Intelligence.

[32]  Fabio Roli,et al.  A theoretical and experimental analysis of linear combiners for multiple classifier systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Alicja Wakulicz-Deja,et al.  Global decision-making system with dynamically generated clusters , 2014, Inf. Sci..

[34]  Isabelle Bloch,et al.  Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[37]  Lukasz Sosnowski,et al.  Election algorithms applied to the global aggregation in networks of comparators , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[38]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[40]  Sung-Bae Cho,et al.  Combining multiple neural networks by fuzzy integral for robust classification , 1995, IEEE Trans. Syst. Man Cybern..

[41]  Michael I. Jordan,et al.  Local linear perceptrons for classification , 1996, IEEE Trans. Neural Networks.

[42]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[44]  Ariën J. van der Wal,et al.  Self-organization and emergent behaviour: distributed decision making in sensor networks , 2013, Int. J. Gen. Syst..

[45]  Andrzej Skowron,et al.  Layered Learning for Concept Synthesis , 2004, Trans. Rough Sets.

[46]  Jerzy Stefanowski,et al.  An Experimental Study of Methods Combining Multiple Classifiers-Diversified both by Feature Selection and Bootstrap Sampling , 2005 .

[47]  Marko Robnik-Sikonja,et al.  Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.

[48]  W. H. Pierce,et al.  IMPROVING RELIABILITY OF DIGITAL SYSTEMS BY REDUNDANCY AND ADAPTION , 1961 .

[49]  D. Black.  The theory of committees and elections , 1959 .

[50]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[51]  Luiz Eduardo Soares de Oliveira,et al.  Feature Selection for Ensembles Using the Multi-Objective Optimization Approach , 2006, Multi-Objective Machine Learning.