Buradasınız

A REVIEW OF INDUCTIVE LEARNING ALGORITHMS

Journal Name:

Publication Year:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. I n the paper knowledge acquisition, induction, inductive learning and the categories of inductive algorithms are discussed, C L S and its family, ID3 and its derivatives, AQ and its family, and recently developed R U L E S family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed respectively. Finally the applications of inductive learning are overviewed.
171-186

REFERENCES

References: 

[1] L i u W.Z. and White A.P. (1991) "A review of inductive learning", i n proc.
Research and Development in Expert Systems VIII, Cambridge, pp. 112-126.
[2] Mrozek, A. (1992) "A nevv method for discovering rules from examples i n
expert systems", Int. J. Man-Machine Studies, 36. pp. 127-143.
[3] Hart A. (1989) "Knovvledge acquisition for expert systems", Chapman
and Hail, London.
[4] Waterman D.A. (1986) "A guide to expert systems", Addison-Wesley,
California.
[5] Quinlan J.R. (1988) "induction, knovvledge and expert systems",in
Artificial intelligence Developments and Applications, Eds J.S. Gero and R.
Stanton, Amsterdam, North-Holland, pp. 253-271.
[6] Weiss S.M. and Kulikowski C.A. (1991) "Computer systems that learn",
Morgan Kaufmann, San Mateo, California.
183
[7] "VVilliams G.J. (1988) "Combining decision trees, initial results from MİL
algorithm", in Artificial intelligence Developments and Applications, Eds: J.S.
Gero and R. Stanton, pp. 273-289.
[8] Devedzic, V. and Velasevic D. (1990) "Features of second generation
expert systems, an extended overview", Eng. Appl. of Al, Vol. 3, Deçember, pp.
255-270.
[9] Nakakuki Y., Koseki Y. and Tanaka M. (1990) "inductive learning in
probabilistic domain", in proc. Eighth National Conf. on Al, Boston, July 29,
August 3, pp. 809-814.
[101 Forsyth R. (1989) "Machine Learning principles and tecniques", Ed: R.
Forsyth, Chapman and Hail, London.
[11] Hancox P.J., Mills W.J. and Reid B.J. (1990) "Artificial intelligence /
expert systems", Ergosyst Associates, Lawrence, Kansas.
[12] Charniak, E. and McDermott, D. (1985) "Introduction to artifical
intelligence", Addison-VVesley, California.
[13] Tanimoto S.L. (1987), "The elements of artifical intelligence", Computer
Science Press, Maryland, USA.
[14] Rubin S.H. (1991) "Expert systems for knowledge aequisition" in proc.
First World Congress on Expert Systems, Vol. 3 Orlando, Florida, December 16¬
19, pp. 1793-1799.
[15] Kodratoff Y. (1988) "Introduction to machine learning", Pitman
Publishing, London.
[16] Al-Attar A. (1991) "Rule induction from mythology to methodology",
Research and Developments in Expert Systems VIII, London, September, pp.
85-103.
[17] Quinlan J.R. (1987a) "Generating production rules from decision trees",
İn proc. Tenth IJCAI-87, Milan, Italy, pp. 304-307.
[18] Quinlan J.R (1990) "Learning logical definitions from relations", in
Machine Learning, 5, Kluwer Publishers, Boston, pp. 239-266.
[19] Shapiro S.C. et. al. (1991) "Encyclopedia of artifical inteligence", Second
edition.
[20] Hunt E.B, Marin J . , and Stone P.J. (1966) "Experiments in iduction",
Academic Press, New York.
[21] Quinlan J.R. (1983) "Learning efficient classification procedures and
their applications to chess end games" in Machine Learning, An Artificial
intelligence Approach, Eds: R.S. Michalski, J.G. Carbonell and T.M. Mitchell,
Morgan Kaufmann, Tiago, Palo Alto, CA, pp. 463-482.
[22] Michalski R.S. (1990) "A theory and methodology of inductive learning",
in Readiııgs in Machine Learning, Eds: J.W. Shavlik and T.G. Dietterich,
Morgan Kaufmann, San Mateo, California, pp. 70-95.
[23] Dietterich, T.G. and Michalski R.S. (1983) "A comparative review of
selected methods for learning from examples", in Machine Learning, an
184
Artificial İntelligence Approach, Vol 1, Eds: R.S. Michalski, J.G. Carbonell and
T.M. Mitchell, Morgan Kaufmann, pp. 41-81.
[24] Evangelos, I.P., et. al (1992) "A minimum entropy approach to rule
learning from examples", I E E E Trans. Systems Man and Cybernetics, 22(4),
July/August, pp. 621-635.
[25] Cheng J . et. al. (1988) "Improved decision trees: a generalised version of
ID3", in proc. Fifth Int. Conf: on Machine Learning, The University of
Michigan, Ann Arbor, Mİ June, 12-14, pp. 100-106.
[26] Mace R. (1974) "Management information and the computer",
Ffaymarket, London.
[27] Schlimmer J .C and Fisher D. (1986) "A case study of incremental
concept induction" in proc. Fifth National Conf on Al, Morgan Kaufmann, San
Mateo, CA, pp. 496-501.
[28] Utgoff P.E., (1988) "ID5; an incremental ID3"In proc. Fifth Int.
Conference on Machine Learning, The University of Michigan Ann Arbor, MI,
June 12-14, pp. 107-120.
[29] Michalski R.S. (1973) "Discovering classification rules using variablevalued
logic system VL1", in Artificial İntelligence Proceedings of the Third
Int. Joint Conf. Stanford, pp. 162-172.
[301 Michalski R.S. (1975) "Synhesis of optimal and quasi-optimal variablevalued
logic formulas", in proc. 1975 Int. Symposium on Multiple-Valued
Logic, Bloomington, May, pp. 76-87.
[31] Chan K.C.C., Ching J.Y. and Wong A.K.C. (1992) "Learning fault
diagnostic rules: a probabilistic inductive inference approach", in Applications
of Al in Engineering VII, Eds: D.E. Gvierson, G. Rzevski and R.A. Adey,
Elsevier Applied Science, Nevv York, pp. 125-142.
" [32] Michalski R.S. and Larson J.B. (1978) "Selection of most represantative
training examples and incremental generation of VL1 hypothesis: the
underlying methodology and the descriptions of programs ESEL and AQ11",
Report No. 867, Department of Computer Science, University of Illinois, Urbana,
Illinois.
[33]Cohen P.R. and Feigenbaum E.A. (1982) "The handbook of artificial
intelligence", Vol. 3, William Kaufmann, California.
[34]Hong J . , Mozetic I. and Michalski R.S. (1986) "AQ15: incremental
learning of attribute-based descriptions from examples, the method and user's
güide", Report ISG 86-5, UIUCDCS-F-86-949, Dept. of Computer Science, Univ of
Illinois, Urbana, Illuıois.
[35] Bloedorn E. and Michalski R.S. (1991) "Data-driven constructive
induction in AQ17-DCI: a method and experiments", Reports of Machine
Learning and inference Laboratory, Center for Artificial intelligence, George
Mason University.
185
[36] Thrun S.B. et al. (1991) "The MONK's problems- a performance
comparison of different learning algorithms", School of Computer Science,
Carnegie Mellon University, Research Report, CMU-CS-91-197, December,
Pittsburg, Pennsylvania.
[37] Wnek J . and Michalski R.S. (1991) "Hypothesis-driven constructive
induction in AQ17: a method and experiments", in proc. Tıvelfth Int. Joint
Conf. on Al, August, Sydney Australia.
[38] Pachovvicz P.W. and Bala J. (1991a) "Improving recognition
effectiveness of noisy texture concepts through optimization of their
descriptions", in proc. 8 th Int. Workshop on Machine Learning, Evanston, pp.
625-629.
[39] Pachowicz P.W. and Bala J . (1991b) "Advancing texture recognition
through machine learning and concept optimisation", Reports of Machine
Learning and inference Laboratory, MLI-6, Artificial intelligence Center,
George Mason University.
[40] Pham, D.T. and Aksoy M.S. (1995a) "RULES; A simple rule extraction
system", Expert Systems with Applications, Vol. 8, No.l, pp. 59-65, USA.
[41] Pham, D.T. and Aksoy M.S. (1993) "An algorithm for automatic rule
induction", Artificial intelligence I/z Engineering, No: 8, pp. 277-282, U.K.
[42] Pham D. T. And Aksoy M.S. (1995b), "A nevv algorithm for inductive
learning", Journal of Systems Eng., No: 5, pp. 115-122, U.K.
[43] Michalski R.S. et al (1986) "The multi-purpose incremental learning
system AQ15 and its testing application to three medical domains", in proc.
National Conf. on Al, Philadelphia, PA, August, pp. 1041-1044.
[44] Quinlan J.R. (1987b), "inductive knovvledge acquisition: a case study", in
Applications of Expert Systems, Ed: J.R. Quinlan, Turing Institute Press, pp.
157-173.
[45] Race P.R and Thomas R.C. (1988), "Rule induction in investment
appraisal", Journal of the Operational Research Society, 38, pp. 1113-1123.
[46] Spiehler E . J . (1987), "Application of machine learning to classification
of glass fragment evidence in forensic science", Seminar Materials, Machine
Learning Seminar, Learned information Ltd., Oxford, and Machine Learning
Research Ltd., Nottingham, London.
[47] Modesitt K.L. (1987) "Space shuttle main engine anomaly data and
inductive knovvledge-based systems: automated corporate expertise", in proc.
Conf Artificial intelligence for Space Applications, Huntsville, Alabama,
November, pp. 1-8.
[48] Dale M.B. McBratney A.B. and Russell J.S. (1989), "On the role of expert
systems and numerical taxonomy in soil classification", Journal of Soil
Science, 40 pp. 223-234.
186
[49] Thorpe J.C., Marr A and Slack R.S. (1989), "Using an expert system to
monitör an automatic stock control system", Journal of the Operational
Research Society, 40, pp. 945-952.
[50] Selby R.W. and Porter A.A. (1988) "Learning from examples: generation
and evaluation of decision trees for software resource analysis", I E E E
Transactions on Software Engineering, 14, pp. 1743-1756.
[51] Carter C and Catlett J . (1987) "Assesing credit card applications using
machine learning", IEEE Expert Intelligent Systems and Their Applications,
FALL, pp. 71-79.
[52] Lirov Y., Rodin E.Y. and Ghosh B.K. (1989) "Automated learning by
tactical decision systems in air combat", Computer and Mathematics with
Application, 18, pp. 151-160.
[53] Batur C , Srinivasan A. and Chan C.C. (1991) "Automated rule-based
model generation for uncertain complex dynamic systems", Eng. Appl. of. Al,
4(5), pp. 359-366.
[54] Pham D.T. and Aksoy M.S. (1993b), 'Dynamic system modelling using a
nevv induction algorithm", (to be published).
[55] Ke M. and Ali M. (1989), "A learning representation and diagnostic
methodology for engine fault diagnosis", in proc. Second Int. Conf. on
Industrial and Engineering Applications of Al and Expert Systems, Vol. 2,
June, 6-9, pp. 824-830.
[56] Harrington P.D. Street T.E. and Voorhees K.J. (1989),"Rule building
expert systems for classification of mass spectra", Analytical Chemistry, 61, pp.
715-719.
[57] Harrington P.D. and Voorhees K.J. (1990) "Multivariate rule-building
expert system", Analytical Chemistry, 62, pp. 729-734.
[58] Scott D.R. (1989) "Classification and identification of mass spectra of
toxic compounds vvith an inductive rule building expert system and
information theory", Analytical Ghimica Açta, 223, pp. 105-121.
[59] Wu X. (1993) "inductive learning: Algorithms and Frontiers", Artificial
intelligence Revieıo, 7, pp. 93-108.
[60] Michalski, R.S. and Stepp R.E. (1983), "Learning from observation:
conceptual clustering", in Machine Learning and Artificial intelligence
Approach, Vol 1, Eds: R.S. Michalski, J.G. Carbonell, and T.M. Mitchell,
Morgan Kaufman, pp. 331-363.

Thank you for copying data from http://www.arastirmax.com