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Knowledge acquisition, Rule extraction, Expert systems, Lenses Dataset, Rule Checker Algorithms
Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.
İbrahim Berkan Aydilek, Abdülkadir Gümüşçü, "Designing a Rule Based Expert Systems for Contact Lenses Patients", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.3, pp.18-26, 2018. DOI:10.5815/ijitcs.2018.03.03
Onisko A, Lucas P, Druzdzel MJ. Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. Lect Notes Artif Int. 2001;2101:283-92.
Eghbali S, Ayatollahi S, Boozarjomehry RB. New expert system for enhanced oil recovery screening in non-fractured oil reservoirs. Fuzzy Set Syst. 2016;293:80-94.
Mehdi KHOSRAVİ, Mohammad YAZDANSHENAS, Mohammad Hossein NEMATİ, Design of an Expert System for Diagnosis of Thyroid Cancer, Cumhuriyet University Faculty of Science Science Journal (CSJ), Vol. 36, No: 3 Special Issue (2015) ISSN: 1300-1949
Nascimento DA, Anunciacao RM, Arnhold A, Ferraz AC, dos Santos A, Zanuncio JC. Expert system for identification of economically important insect pests in commercial teak plantations. Comput Electron Agr. 2016;121:368-73.
Ramirez G, Valenzuela MA, Lorenz RD. Expert System for the Detection of Condensate Accumulation Inside Dryer Cylinders During Section Starting. Ieee T Ind Appl. 2015;51(2):1427-37.
Keles A. Expert Doctor Verdis: Integrated medical expert system. Turk J Electr Eng Co. 2014;22(4):1032-43.
Liao SH. Expert system methodologies and applications - a decade review from 1995 to 2004. Expert Syst Appl. 2005;28(1):93-103.
Sai TK, Reddy KA. New Rules Generation From Measurement Data Using an Expert System in a Power Station. Ieee T Power Deliver. 2015;30(1):167-73.
Ikram A, Qamar U. Developing an expert system based on association rules and predicate logic for earthquake prediction. Knowl-Based Syst. 2015;75:87-103.
Bashir S, Qamar U, Khan FH, Javed MY. MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble. Arab J Sci Eng. 2014;39(11):7771-83.
Balouchi B, Nikoo MR, Adamowski J. Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: Application of different types of ANNs and the M5P model tree. Appl Soft Comput. 2015;34:51-9.
K.Mani, R.Akila,"Enhancing the Performance in Generating Association Rules using Singleton Apriori", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.1, pp.58-64, 2017. DOI: 10.5815/ijitcs.2017.01.07
Coussement K, Benoit DF, Antioco M. A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection. Decis Support Syst. 2015;79:24-32.
Dongjin Park, Taeho Cho,"A Fuzzy Rule-based Key Re-Distribution Decision Scheme of Dynamic Filtering for Energy Saving in Wireless Sensor Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.1-8, 2017. DOI: 10.5815/ijitcs.2017.04.01
Yin JT, Chen DW, Li LX. Intelligent Train Operation Algorithms for Subway by Expert System and Reinforcement Learning. Ieee T Intell Transp. 2014;15(6):2561-71.
Sarkar S, Sharma T, Baral A, Chatterjee B, Dey D, Chakravorti S. An Expert System Approach for Transformer Insulation Diagnosis combining Conventional Diagnostic Tests and PDC, RVM Data. Ieee T Dielect El In. 2014;21(2):882-91.
Ighoyota Ben Ajenaghughrure, P. Sujatha, Maureen I. Akazue, "Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.10, pp.13-28, 2017. DOI: 10.5815/ijitcs.2017.10.02
Sahar Abdalla Elmubarak, Adil Yousif, Mohammed Bakri Bashir,"Performance based Ranking Model for Cloud SaaS Services", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.1, pp.65-71, 2017. DOI: 10.5815/ijitcs.2017.01.08
Omid M. Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier. Expert Syst Appl. 2011;38(4):4339-47.
M. Negnevitsky, Artificial intelligence: a guide to intelligent systems, Pearson Education, 2005
Chang LL, Zhou ZJ, You Y, Yang LH, Zhou ZG. Belief rule based expert system for classification problems with new rule activation and weight calculation procedures. Inform Sciences. 2016;336:75-91.
D'Haen J, Van den Poel D, Thorleuchter D, Benoit DF. Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decis Support Syst. 2016;82:69-78.
Yin JT, Chen DW, Li YD. Smart train operation algorithms based on expert knowledge and ensemble CART for the electric locomotive. Knowl-Based Syst. 2016;92:78-91.
Ho HF, Li ST. Using mutually validated memories of experts for case-based knowledge systems. Knowl-Based Syst. 2015;86:102-15.
Laasri EHA, Akhouayri ES, Agliz D, Zonta D, Atmani A. A fuzzy expert system for automatic seismic signal classification. Expert Syst Appl. 2015;42(3):1013-27.
Seok J, Kasa-Vubu J, DiPietro M, Girard A. Expert system for automated bone age determination. Expert Syst Appl. 2016;50:75-88.
Ghanbari A, Kazemi SMR, Mehmanpazir F, Nakhostin MM. A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl-Based Syst. 2013;39:194-206.
Murianaa Cinzia, Piazzaa Tommaso, Vizzinib Giovanni, An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs. Knowl-Based Syst. 2016:97:1-10
Lee KC, Lee S. A causal knowledge-based expert system for planning an Internet-based stock trading system. Expert Syst Appl. 2012;39(10):8626-35.
Xavier D, Crespo B, Fuentes-Fernandez R. A rule-based expert system for inferring functional annotation. Appl Soft Comput. 2015;35:373-85.
Zhou Zude, Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation: Methods for System Self-Organization, Learning, and Adaptation, IGI Global, 2010
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
Brent Martin (1995). Instance-Based learning: Nearest Neighbor With Generalization. Hamilton, New Zealand.
Sylvain Roy (2002). Nearest Neighbor With Generalization. Christchurch, New Zealand
Cendrowska J. Prism - an Algorithm for Inducing Modular Rules. Int J Man Mach Stud. 1987;27(4):349-70.
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.]
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
T. A. Nguyen , W. A. Perkins , T. J. Laffey and D. Pecora, "Checking an expert sytems knowledge base for consistency and completeness", Proc. IJCAI-85, pp. 375-378, 1985
M. Suwa, A.C. Scott, E.H. Shortliffe, An approach to verifying completeness and consistency in a rule-based expert system, AI Mag., 3 (3) (1982), pp. 16–21