Definition
Structured induction is a method of applying machine learning in which a model for a task is learned using a representation where some of the components are themselves the outputs of learned models for specified sub-tasks. The idea was inspired by structured programming (Dahl et al. 1972), in which a complex task is solved by repeated decomposition into simpler sub-tasks that can be easily analyzed and implemented. The approach was first developed by Alen Shapiro (1987) in the context of constructing expert systems by decision tree learning, but in principle it could be applied using other learning methods.
Motivation and Background
Structured induction is designed to solve complex learning tasks for which it is difficult a priori to obtain a set of attributes or features in which it is possible to represent an accurate approximation of the target hypothesis reasonably concisely. In Shapiro’s approach, a hierarchy of decision trees is learned, where in each tree of the...
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Recommended Reading
Dahl OJ, Dijkstra EW, Hoare CAR (eds) (1972) Structured programming. Academic Press, London
Feigenbaum EA (1977) The art of artificial intelligence: themes and case studies of knowledge engineering. In: Reddy R (ed) Proceedings of the fifth international conference on artificial intelligence (IJCAI’77). William Kaufmann, Los Altos, pp 1014–1029
Gaines B (1996) Transforming rules and trees into comprehensible knowledge structures. In: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. MIT Press, Cambridge, MA, pp 205–226
Michie D (1982) Measuring the knowledge-content of expert programs. Bull Inst Math Appl 18(11/12):216–220
Muggleton S (1987) Duce, an oracle-based approach to constructive induction. In: IJCAI’87. Morgan Kaufmann, Los Altos, pp 287–292
Pagallo G, Haussler D (1990) Boolean feature discovery in empirical learning. Mach Learn 5:71–99
Quinlan JR (1983) Learning efficient classification procedures and their application to chess end games. In: Michalski R, Carbonnel J, Mitchell T (eds) Machine learning: an artificial intelligence approach. Tioga, Palo Alto, pp 464–482
Razzak MA, Hassan T, Pettipher R (1984) Extran-7: a Fortran-based software package for building expert systems. In: Bramer MA (ed) Research and development in expert systems. Cambridge University Press, Cambridge, pp 23–30
Shapiro A, Niblett T (1982) Automatic induction of classification rules for a chess endgame. In: Clarke MRB (ed) Advances in computer chess, vol 3. Pergamon Press, Oxford, pp 73–91
Shapiro AD (1987) Structured induction in expert systems. Turing Institute Press with Addison Wesley, Wokingham
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
Zhang J, Honavar V (2003) Learning decision tree classifiers from attribute value taxonomies and partially specified data. In: ICML-2003: Proceedings of the twentieth international conference on machine learning. AAAI Press, Menlo Park
Zheng Z (1995) Constructing nominal X-of-N attributes. In: Proceedings of the fourteenth International joint conference on artificial intelligence (IJCAI’95). Morgan Kaufmann, Los Altos, pp 1064–1070
Zupan B, Bohanec M, Demsar J, Bratko I (1999) Learning by discovering concept hierarchies. Artif Intell 109:211–242
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Bain, M. (2017). Structured Induction. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_796
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_796
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