Fuzzy Activation of Rough Cognitive Ensembles Using OWA Operators

Rough Cognitive Ensembles (RCEs) can be defined as a multiclassifier system composed of a set of Rough Cognitive Networks (RCNs), each operating at a different granularity degree. While this model is capable of outperforming several traditional classifiers reported in the literature, there is still room for enhancing its performance. In this paper, we propose a fuzzy strategy to activate the RCN input neurons before performing the inference process. This fuzzy activation mechanism essentially quantifies the extent to which an object belongs to the intersection between its similarity class and each granular region in the RCN topology. The numerical simulations have shown that the improved ensemble classifier significantly outperforms the original RCE model for the adopted datasets. After comparing the proposed model to 14 well-known classifiers, the experimental evidence confirms that our scheme yields very promising classification rates.

[1]  Yiyu Yao,et al.  Three-Way Decision: An Interpretation of Rules in Rough Set Theory , 2009, RSKT.

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[4]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[5]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..

[6]  Witold Pedrycz,et al.  Granular computing: an introduction , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[7]  Eibe Frank,et al.  Speeding Up Logistic Model Tree Induction , 2005, PKDD.

[8]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[9]  Nikunj C. Oza,et al.  Decimated input ensembles for improved generalization , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[10]  Witold Pedrycz,et al.  Granular classifiers and their design through refinement of information granules , 2017, Soft Comput..

[11]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[14]  Witold Pedrycz,et al.  From Fuzzy Cognitive Maps to Granular Cognitive Maps , 2014, IEEE Trans. Fuzzy Syst..

[15]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[16]  Francis K. H. Quek,et al.  Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets , 2003, Pattern Recognit..

[17]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[18]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..

[19]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[20]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[21]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[22]  Koen Vanhoof,et al.  Rough cognitive ensembles , 2017, Int. J. Approx. Reason..

[23]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[24]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[25]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[26]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[27]  Bart Kosko,et al.  Hidden patterns in combined and adaptive knowledge networks , 1988, Int. J. Approx. Reason..

[28]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[29]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[30]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[31]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[32]  Haijia Shi.  Best-first Decision Tree Learning , 2007 .

[33]  Koen Vanhoof,et al.  Rough Cognitive Networks , 2016, Knowl. Based Syst..

[34]  Harry Zhang,et al.  A Fast Decision Tree Learning Algorithm , 2006, AAAI.