A Novel Fuzzy Linguistic Fusion Approach to Naive Bayes Classifier for Decision Making Applications
Abstract
Naive Bayes is one of the most widely used classifier algorithms in various data mining problems. The performance of the Naïve Bayes Classifier is comparable to other classifiers as it yields impressive results in multiple applications. An increase in the performance of the Naive Bayes Classifier is possible by identifying and forming segments of the data handled by the classifier. In this paper, a novel fuzzy-based fusion approach to selected quantitative features is proposed. The approach is used to improve the prediction accuracy of the Naive Bayes Classifier (NBC). The linguistic computing model with fusion operators, using ranked indexes of the linguistic terms in the dataset is made use in this proposed approach. Fuzzy values are generated only for the numerical attributes in the initial phase using 2-tuple linguistic computations. The equivalent real value computations are performed in order to express the results in the initial domain of the expression. These computations ensure improved comprehensiveness of the results of the classifier. The model incorporates the concepts of linguistic terms, fuzzy logic, fusion methods, and aggregation operations to the classical Naïve Bayes Classifier. Such incorporation is used to improve the performance of the classifier in various decision-making applications. The proposed model is validated using a standard benchmark dataset–Stat log Heart disease dataset. It is obtained from the UCI Machine Learning Repository. The proposed Linguistic Fuzzy Naive Bayes Classifier showed better accuracy compared to the Simple Naive Bayes Classifier performance.
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J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques,†Third. Waltham, USA: Elsevier Inc., 2012.
R. J. Roiger, “Data Mining A Tutorial-Based Primer,†2nd ed. London: CRC Press, Taylor & Francis Group, 2017.
L.A. Zadeh, “The concept of a linguistic variable and its applications to approximate reasoning,†Information Sciences, vol.8, pp. 199–249, 1975.
L.A. Zadeh, “Fuzzy Logic = Computing with Words,†IEEE Transactions on Fuzzy Systems, vol.4, pp. 103–111, 1996.
F. Herrera and L. Martinez, “An approach for combining linguistic and numerical information based on 2-tuple fuzzy linguistic representation model in decision-making,†International Journal of Uncertainty, Fuzziness, Knowledge-Based Systems, vol. 8, pp. 539–562, 2000.
F. Herrera, E. Herrera-Viedma, and L. MartÃnez, “A fusion approach for managing multi-granularity linguistic terms sets in decision making,†Fuzzy Sets Systems., vol. 114, pp. 43–58, 2000.
C. C. Li, Y. C. Dong, F. Herrera, E. Herrera-Viedma, and L. Martinez, “Personalised individual semantics in computing
with words for supporting linguistic group decision making. An application on consensus reaching,†information
Fusion, vol. 33, pp. 29-40, 2017.
R. Urena, F. Chiclana, J. A. Morente-Molinera, and E. Herrera-Viedma, “Managing incomplete preference relations in decision making: a review and future trends,†Information Sciences, vol. 302, pp. 14–32, 2015.
R. Urena, F. Chiclana, and E. Herrera-Viedma, “Consistency based completion approaches of incomplete preference relations in uncertain decision contexts,†in: IEEE International Conference on Fuzzy Systems, 2015, pp. 1–6.
M. Brunelli, and M. Fedrizzi, “Boundary properties of the inconsistency of pair wise comparisons in group decisions,†European Journal of Operational Research, vol. 240, pp. 765–773, 2015.
S. Kubler, W. Derigent, A. Voisin, J. Robert, and Y. Le Traon, “Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices,†in: IEEE International Conference on Fuzzy Systems, 2017, pp.1–7.
R. Verma and B. D. Sharma, “Trapezoid Fuzzy Linguistic Prioritized Weighted Average Operators and Their Application to Multiple Attribute Group Decision Making,†Journal of Uncertainty Analysis and Applications, vol. 2, pp. 1-19, 2014.
R. Verma, “Multiple Attribute Group Decision Making based on Generalized Trapezoid Fuzzy Linguistic Prioritized Weighted Average Operator,†International Journal of Machine Learning and Cybernetics, 2016
R. Verma, “Prioritised Information Fusion Method for Triangular Fuzzy Information and Its Application to Multiple Attribute Decision Making,†International Journal of Uncertainty Fuzziness and Knowledge Based Systems, vol. 24, no. 2, pp. 265-289, 2016.
I. Beg. and T. Rashid, “An intuitionistic 2â€tuple linguistic information model and aggregation operatorsâ€, International Journal of Intelligent Systems, Vol. 31 No. 6, pp. 569-592, 2016.
H. Liao, X. Mi, Z. Xu, J. Xu, and F. Herrera, "Intuitionistic Fuzzy Analytic Network Process," in IEEE Transactions on Fuzzy Systems, vol.26, no.5, pp. 2578-2590, 2018.
S. M. Chen. and W. H. Han, “A new multiattribute decision making method based on multiplication operations of interval-valued intuitionistic fuzzy values and linear programming methodology,†Information Sciences, vol. 429, no. 2, pp. 421-432, 2018.
S.H. Cheng, “Autocratic multiattribute group decision making for hotel location selection based on interval-valued intuitionistic fuzzy sets,†Information Sciences, vol. 427, no. 1, pp. 77-87, 2017.
J. Deepa, and S. Kumar, “Improved accuracy function for interval-valued intuitionistic fuzzy sets and its application to multi-attributes group decision making,†Cybernetics and Systems, vol. 49, no. 1, pp. 64-76, 2018.
P. D. Liu, and S. M. Chen, “Multiattribute group decision making based on intuitionistic 2-tuple linguistic information,†Information Sciences, vol. 430-431, no. 1, pp. 599-619, 2018.
P. Wang, X. H. Xu, J. Q. Wang, and C. G. Cai, “Interval-valued intuitionistic linguistic multi-criteria group decision-making method based on the interval 2-tuple linguistic informationâ€, Journal of Intelligent & Fuzzy Systems, vol. 33, no. 2, pp. 985-994, 2017.
F. T. Bahari, and M. S. Elayidom, “An Enhanced Analytic CRM Framework Using Symbolic Fuzzy Approach in Decision Making Applicationsâ€, Journal of Advanced Research in Dynamical & Control Systems, vol. 10, 15-Special Issue, 2018.
F. Chiclana, F. Mata, L. G. Perez, and E. H. Viedma, “Typeâ€1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology: Application to Eurobonds Credit Risk Evaluation,†International Journal of Intelligent Systems, vol.33, pp.1071-1088, 2018.
A. Peña, I. Bonet, C. Lochmuller, F. Chiclana, and M. Góngora, “An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management,†Expert Systems with Applications, vol. 98, pp.11-26, 2018.
G. Yazgı Tütüncu, and Necla Kayaalp, “An aggregated fuzzy naive bayes data classifier,†Journal of Computational and Applied Mathematics, vol. 286, pp. 17–27, 2015.
H. P. Störr, “A compact fuzzy extension of the naive bayesian classification algorithm,†Proceedings of the Third International Conference on Intelligent Technologies and Vietnam-Japan Symposium on Fuzzy Systems and Applications, pp.172-177, 2002.
Y. Z. Xi, W. T. Xue, and S. L. Joon, “Fuzzy naive bayesian for constructing regulated network with weights,†Bio-Medical Materials and Engineering, vol. 26, pp. S1757–S1762, 2015.
Y. Tang, W. Pan, H. Li, and Y. Xu, “Fuzzy naive bayes classifier based on fuzzy clustering", Proceedings of 2002 IEEE International Conference on System, Man and Cybernetics, 2002.
Y. Tang, and Y. Xu, “Application of fuzzy naive bayes and a real-valued genetic algorithm in identification of fuzzy model, Information Sciences, v.169, p.205-225, 2005.
S. Palaniappan, and R. Awang, “Intelligent heart disease prediction system using data mining techniques,†IEEE/ACS International Conference on Computer Systems and Applications, 2008.
S. B. Patil, and Y. S. Kumaraswamy, “Extraction of significant patterns from heart disease warehouses for heart attack prediction,†International Journal of Computer Science and Network Security, vol. 9, pp. 228–235, 2009.
Jan Bohacik, and Michal Zabovsky, “Naive bayes for statlog heart database with consideration of data specifics,†IEEE 14th International Scientific Conference on Informatics, 2017.
O. W. Samuel, G. M. Asogbon, A. K. Sangaiah, P. Fang, and G. Li, “An integrated decision support system based on ANN and Fuzzy AHP for heart failure risk prediction,†Expert Systems with Applications, vol. 68, pp.163−172, 2017.
K. Uyar, A. Ilhan, “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks,†Procedia Computer Science, vol. 120, pp.588-593, 2017.
G. T. Reddy, N. Khare, “An Efficient System for Heart Disease Prediction using Hybrid OFBAT with Rule-Based Fuzzy Logic Model,†Journal of Circuits, Systems, and Computers, vol.26, 2017
L. Martinez, D. Ruan, F. Herrera, E. Herrera-Viedma, and P.P. Wang, “Linguistic decision making: tools and applications,†Information Sciences, vol.179, pp. 2297–2298, 2009.
L. Martinez, “Computing with words in linguistic decision making: Analysis of linguistic computing models,†IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010.
I. H. Witten, E. Frank, and M. A. Hall, “Practical Machine Learning Tools and Techniques,†3rd Edition. Burlington, MA, USA: Morgan Kaufman Publishers, 2011.
UCI Repository of Machine Learning Databases, http://archive.ics.uci.edu/ml/datasets/Statlog+%28Heart%29.
DOI: http://dx.doi.org/10.18517/ijaseit.10.5.8186
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