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2016 | OriginalPaper | Chapter

ADX Algorithm for Supervised Classification

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Abstract

In this paper, a final version of the rule based classifier (ADX) is presented. ADX is an algorithm for inductive learning and for later classification of objects. As is typical for rule systems, knowledge representation is easy to understand by a human. The advantage of ADX algorithm is that rules are not too complicated and for most real datasets learning time increases linearly with the size of a dataset. The novel elements in this work are the following: a new method for selection of the final ruleset in ADX and the classification mechanism. The algorithm’s performance is illustrated by a series of experiments performed on a suitably designed set of artificial data.

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Literature
1.
go back to reference Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases, Santiago, Chile Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large databases, Santiago, Chile
2.
go back to reference Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudso J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell Lymphoma identified by expression profiling. Nature 403:503–511CrossRef Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudso J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell Lymphoma identified by expression profiling. Nature 403:503–511CrossRef
3.
go back to reference Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Monterey Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Monterey
4.
go back to reference Clarc P, Niblett T (1989) The CN2 induction algorithm. Mach. Learn. 3:261–283 Clarc P, Niblett T (1989) The CN2 induction algorithm. Mach. Learn. 3:261–283
5.
go back to reference Cohen W (1995) Fast effective rule induction. In: Machine learning: proceedings of the twelfth international conference, Lake Tahoe, California Cohen W (1995) Fast effective rule induction. In: Machine learning: proceedings of the twelfth international conference, Lake Tahoe, California
6.
go back to reference Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans. Inform. Theory, IT-13(1):2127 Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans. Inform. Theory, IT-13(1):2127
7.
go back to reference Dramiński M (2004) ADX Algorithm: a brief description of a rule based classifier. In: Proceedings of the new trends in intelligent information processing and web mining IIS’2004 symposium. Springer, Zakopane, Poland Dramiński M (2004) ADX Algorithm: a brief description of a rule based classifier. In: Proceedings of the new trends in intelligent information processing and web mining IIS’2004 symposium. Springer, Zakopane, Poland
8.
go back to reference Dramiński M (2005) Description and practical application of rule based classifier ADX, proceedings of the first Warsaw International Seminar on intelligent systems, WISIS (2004). In: Dramiski M, Grzegorzewski P, Trojanowski K, Zadrozny S (eds) Issues in intelligent systems. Models and techniques. ISBN 83-87674-91-5, Exit 2005 Dramiński M (2005) Description and practical application of rule based classifier ADX, proceedings of the first Warsaw International Seminar on intelligent systems, WISIS (2004). In: Dramiski M, Grzegorzewski P, Trojanowski K, Zadrozny S (eds) Issues in intelligent systems. Models and techniques. ISBN 83-87674-91-5, Exit 2005
9.
go back to reference Fayyad UM, Irani KB (1992) On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8:87–102MATH Fayyad UM, Irani KB (1992) On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8:87–102MATH
10.
go back to reference Fix E, Hodges JL (1951) Discriminatory analysis nonparametric discrimination: Consistency properties, Project 21–49-004, Report no. 4, USAF School of Aviation Medicine, Randolph Field, pp 261–279 Fix E, Hodges JL (1951) Discriminatory analysis nonparametric discrimination: Consistency properties, Project 21–49-004, Report no. 4, USAF School of Aviation Medicine, Randolph Field, pp 261–279
11.
go back to reference Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh M, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of Cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537CrossRef Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh M, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of Cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537CrossRef
12.
go back to reference Grzymala-Busse JW (2003) MLEM2-Discretization during rule induction, intelligent information processing and web mining. In: Proceedings of the international IIS:IIPWM’03 conference held in Zakopane, Poland Grzymala-Busse JW (2003) MLEM2-Discretization during rule induction, intelligent information processing and web mining. In: Proceedings of the international IIS:IIPWM’03 conference held in Zakopane, Poland
13.
go back to reference Hastie T, Tibshirani R, Friedman J (2001) Elements of statistical learning: data mining, inference and prediction. Springer, New YorkCrossRef Hastie T, Tibshirani R, Friedman J (2001) Elements of statistical learning: data mining, inference and prediction. Springer, New YorkCrossRef
14.
go back to reference Kaufman KA, Michalski RS (1999) Learning from inconsistent and noisy data: the AQ18 approach. In: Proceedings of the eleventh international symposium on methodologies for intelligent systems (ISMIS’99), Warsaw, pp 411–419 Kaufman KA, Michalski RS (1999) Learning from inconsistent and noisy data: the AQ18 approach. In: Proceedings of the eleventh international symposium on methodologies for intelligent systems (ISMIS’99), Warsaw, pp 411–419
15.
go back to reference Michalski RS, Kaufman KA (2001) The AQ19 system for machine learning and pattern discovery: a general description and user’s guide, reports of the machine learning and inference laboratory, MLI 01–2. George Mason University, Fairfax Michalski RS, Kaufman KA (2001) The AQ19 system for machine learning and pattern discovery: a general description and user’s guide, reports of the machine learning and inference laboratory, MLI 01–2. George Mason University, Fairfax
17.
go back to reference Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishing, DordrechtCrossRefMATH Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishing, DordrechtCrossRefMATH
18.
go back to reference Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann
19.
go back to reference Quinlan JR (1986) Induction of decision trees. Machine learning 1:81–106 Quinlan JR (1986) Induction of decision trees. Machine learning 1:81–106
20.
go back to reference Stefanowski J (1998) On rough set based approaches to induction of decision rules. In: Polkowski L, Skowron A (eds) Rough sets in data mining and knowledge discovery, Physica-Verlag, pp 500–529 Stefanowski J (1998) On rough set based approaches to induction of decision rules. In: Polkowski L, Skowron A (eds) Rough sets in data mining and knowledge discovery, Physica-Verlag, pp 500–529
22.
go back to reference Wittenn IH, Eibe F (2005) Weka 3: data mining software in Java, data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco Wittenn IH, Eibe F (2005) Weka 3: data mining software in Java, data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
Metadata
Title
ADX Algorithm for Supervised Classification
Author
Michał Dramiński
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-18781-5_3

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