Performance evaluation of Baldwin's fuzzy reasoning for large knowledge bases

• Autorzy: Kudłacik P.
• Języki publikacji: EN

Abstrakty

EN

The paper compares performance of Baldwin's fuzzy reasoning based on a fuzzy truth value with the fastest available solutions. The analysis is important in order to locate areas where improvement of the first is the most significant. Potential fast approach based on the fuzzy truth value would be very interesting for many users applying fuzzy systems to solve problems involved with complex knowledge bases. Particularly, all research considering an analysis of genes employing DNA microarrays. Such methods very often generate rules with thousands of atomic premises. The most valuable advantage of Baldwin's reasoning is preserving a fuzzy relation between a fact and a premise in the inference process, where other solutions, especially those commonly used, usually reduce it to only one value. Obtaining the method which, from computation time point of view, is comparable with common approaches but offers more advanced process of fuzzy reasoning, would be a significant achievement. The goal of this analysis is to prepare the future research considering development of Baldwin's method, which computational complexity is comparable to simple, fast and widely used solutions like systems based on the approach of Mamdani and Assilan or Larsen.

Słowa kluczowe

EN

fuzzy reasoning; fuzzy systems; fuzzy sets

PL

wnioskowanie rozmyte; systemy rozmyte; zbiory rozmyte

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 20
• Strony: 29--38
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Kudłacik P.

• University of Silesia, Institute of Computer Science, ul. Będzińska 39, 41-200 Sosnowiec, Poland

Bibliografia

• [1] BALDWIN J.F. A new approach to approximate reasoning using a fuzzy logic. Fuzzy Sets and Systems, 1979, No. 2, pp. 309-325.
• [2] BALDWIN J.F, GUILD N.C.F., Feasible algorithms for approximate reasoning using fuzzy logic. Fuzzy Sets and Systems, 1980, 2:225–251.
• [3] BELLMAN R.E., ZADEH L.A., Modern Uses of Multiple-Valued Logic, chapter Local and Fuzzy Logics, 1977, pp. 103-165.
• [4] BRINTHA S.J., BHUVANESWARI V., Clustering microarray gene expression data using type 2 fuzzy logic. 3rd National Conference on Emerging Trends and Applications in Computer Science (NCETACS) , 2012, pp. 147-151.
• [5] CHUANG C.L., CHEN C.M., SHIEH G.S., JIANG J.A., A neuro-fuzzy inference system to infer gene-gene interactions based on recognition of microarray gene expression patterns. IEEE Congress on Evolutionary Computation (CEC 2007) , 2007, pp. 904-910.
• [6] CHIU S.H., TSAI M.H., WU H.C., CHEN W.C., SHRESTHA U., KUO S., Application of Fuzzy c-Means and Self-organizing maps for genes clustering in mouse brain microarray data analysis. 9th Int. Conf. Fuzzy Systems and Knowledge Discovery (FSKD) , 2012, pp. 1104-1108.
• [7] CZOGAŁA E., ŁESKI J., Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica-Verlag, Springer-Verlag Comp., Heidelberg, 2000.
• [8] FUTSCHIK M.E., KASABOV N.K., Fuzzy clustering of gene expression data. Proc. 2002 IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE'02), 2002, Vol. 1, pp. 414-419.
• [9] HAVENS T.C., KELLER J.M., POPESCU M., BEZDEK J.C., MACNEAL REHRIG, E., APPEL H.M., SCHULTZ J.C., Fuzzy cluster analysis of bioinformatics data composed of microarray expression data and gene ontology annotations. Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2008) , 2008, pp. 1-6.
• [10] HE Y., TANG Y., ZHANG Y.Q., SUNDERRAMAN R., Mining fuzzy association rules from microarray gene expression data for leukemia classification. IEEE Int. Conf. Granular Computing, 2006, pp. 461-464.
• [11] HO S.Y., HSIEH C.H., CHEN H.M., HUANG H.L., Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis. Biosystems, 2006, No. 85, pp. 165-176.
• [12] KUDŁACIK P., Advantages of an Approximate Reasoning Based on a Fuzzy Truth Value. Journal of Medical Informatics & Technologies, 2010, Vol. 16, pp. 125-132.
• [13] KUDŁACIK P., Operations on fuzzy sets with piece-wise linear membership function (in Polish), Studia Informatica, 2008, 29(3A):91–111.
• [14] KUDŁACIK P., Structure of knowledge base in FUZZLIB library (in Polish). Proc. 4th Conf. Databases: Application and Systems'10. Ustroń 25-28 May 2010, Studia Informatica, 2010, 31(2A):469–478.
• [15] LARSEN P.M., Industrial Applications of fuzzy logic control. International Journal of Man-Machine Studies, 1980, No. 12, Vol. 1, pp. 3-10.
• [16] MA P.C.H., CHAN K.C.C., Incremental Fuzzy Mining of Gene Expression Data for Gene Function Prediction. IEEE Trans. Biomedical Engineering, 2011, Vol. 58, Issue 5, pp. 1246-1252.
• [17] MAJI, P., PAUL S., Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data. IEEE/ACM Trans on Computational Biology and Bioinformatics, 2012, Vol. PP , Issue 99, pp. 1.
• [18] MAMDANI E.H., ASSILAN S., An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 1975, 20(2):1–13.
• [19] MARGHNY M.H., EL-SEMMAN I.E., Extracting fuzzy classification rules with gene expression programming. Proc. Int. Conf. Artificial Intelligence and Machine Learning (AIML), Cairo, Egypt, 2005.
• [20] MOHAMMADI A., SARAEE M.H., Estimating Missing Value in Microarray Data Using Fuzzy Clustering and Gene Ontology. IEEE Int. Conf. Bioinformatics and Biomedicine (BIBM '08) , 2008, pp. 382-385.
• [21] PAUL A., SIL J., Sample selection of microarray data using rough-fuzzy based approach. World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) , 2009, pp. 379-384.
• [22] PEREZ M., RUBIN, D.M., SCOTT, L.E., MARWALA, T., STEVENS, W. A Hybrid Fuzzy-SVM classifier, applied to gene expression profiling for automated leukaemia diagnosis. Proc. of IEEE 25th Convention of Electrical and Electronics Engineers, Israel, 2008.
• [23] SCHAEFER G., NAKASHIMA T., YOKOTA Y., ISHIBUCHI H., Fuzzy Classification of Gene Expression Data. Proc. IEEE Int. Conf. Fuzzy Systems, 2007, pp. 1-6.
• [24] VERMA N.K., GUPTA P., AGRAWAL P., YAN CUI, Fuzzy rule based unsupervised approach for salient gene extraction. IEEE Applied Imagery Pattern Recognition Workshop (AIPRW) , 2009, pp. 1-5.
• [25] WANG Y.P., GUNAMPALLY M., CHEN J, BITTEL D., BUTLER M.G., CAI W.W., Accurate Quantification of Gene Expression using Fuzzy Clustering Approaches. IEEE Int. Workshop on Genomic Signal Processing and Statistics (GENSIPS 2007), 2007, pp. 1-4.
• [26] WANG Z.; PALADE V., A Comprehensive Fuzzy-Based Framework for Cancer Microarray Data Gene Expression Analysis. Proc. 7th IEEE Int. Conf. Bioinformatics and Bioengineering (BIBE 2007) , 2007, pp. 1003-1010.
• [27] WU, G.P.K., CHAN K.C.C., WONG A.K.C., BIN WU, Unsupervised discovery of fuzzy patterns in gene expression data. Proc. IEEE Int. Conf. Bioinformatics and Biomedicine (BIBM) , 2010, pp. 269 - 273.
• [28] ZADEH L.A., Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics, 1973, No. 3, Vol. 1, pp. 28-44.
• [29] ZADEH L.A., The concept of a linguistic variable and it’s application to approximate reasoning. parts 1-3. Information Science, 1975, No. 8, Vol. 8-9, pp. 199-249, 301-357, 43-80.
• [30] ZADEH L.A., Fuzzy logic and approximate reasoning. Syntheses, 1975, No. 30, pp. 407-428.