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--- abstract: 'Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning. ' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - stopofeger@yahoo.com creators_name: - family: Iqbal given: Ridwan Al honourific: '' lineage: '' date: 2011-01-25 date_type: completed datestamp: 2011-02-16 19:49:38 department: ~ dir: disk0/00/00/71/80 edit_lock_since: ~ edit_lock_until: 0 edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 7180 fileinfo: /style/images/fileicons/application_pdf.png;/7180/1/RASCAL.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: ~ issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'Rule based learning, hybrid learning, virtual sample, virtual example, artificial sample,artificial example,pruning dataset' lastmod: 2011-03-11 08:57:50 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: ~ refereed: FALSE referencetext: "[Abu�Mostafa, 1995] Abu-Mostafa, Y.S., 1995. Hints. Neural Computation, (7).\r\n[Aha et al., 1991] Aha, D., Kibler, D. & & Albert, M., 1991. Instance-based learning algorithms. Machine learning, 6, pp.37-66.\r\n[Decoste & Sch�lkopf, 2002] Decoste, D. & Sch�lkopf, B., 2002. Training Invariant Support. Machine Learning, 46, pp.161-90.\r\n[Kearns & Vazirani, 1994] Kearns, M. & Vazirani, U., 1994. An Introduction to Computational Learning Theory. MIT Press.\r\n[Mahoney & Mooney, 1992] Mahoney, J.J. & Mooney, R.J., 1992. Combining Symbolic and Neural Learning to Revise Probabilistic Theories. In Proceedings of the 1992 Machine Learning Workshop on Integrated Learning in Real Domains., 1992.\r\n[Mitchell, 1997] Mitchell, T.M., 1997. Artificial neural networks. In Mitchell, T.M. Machine learning. McGraw-Hill Science/Engineering/Math. pp.81-126.\r\n[Mitchell, 1997] Mitchell, T.M., 1997. Concept learning and general to specific ordering. In Mitchell, T.M. Machine learning. McGraw-Hill. pp.20-50.\r\n[Niyogi et al., 1998] Niyogi, P., Girosi, F. & Poggio, T., 1998. Incorporating Prior Information in Machine Learning by Creating Virtual Examples. Proceedings of IEEE, 86, pp.2196-209.\r\n[Pazzani et al., 1991] Pazzani, M., Brunk, C. & & Silverstein, G., 1991. A knowledge-intensive approach to learning relational concepts. In Proceedings of the Eighth International Workshop on Machine Learning. San Francisco, 1991.\r\n[Pazzani et al., 1997] Pazzani, M., Mani, S. & Shankle, W.R., 1997. Comprehensible knowledge discovery in databases. In CogSci-97., 1997.\r\n[Quinlan, 1993] Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.\r\n[Sassano, 2003] Sassano, M., 2003. Virtual Examples for Text Classification with Support Vector Machines. In Proceedings of the 2003 conference on Empirical methods in natural language processing., 2003.\r\n[Sch�lkopf et al., 1996] Sch�lkopf, B., Burges, C. & Vapnik, a.V., 1996. Incorporating invariances in support vector. In Proceedings of ICANN, Springer Lecture notes in Computer Science., 1996.\r\n[Towell & Shavlik, 1994] Towell, G.G. & Shavlik, J.W., 1994. Knowledge-based artificial neural networks. Artif. Intel., 70, pp.50-62.\r\n[Vapnik, 1998] Vapnik, V.N., 1998. Statistical Learning Theory. New York: Wiley.\r\n[Yu, 2007] Yu, T., 2007. Incorporating Prior Domain Knowledge into Inductive Machine Learning Its implementation in contemporary capital markets. PhD Thesis. Sydney, Australia: University of Technology Sydney.\r\n\r\n" relation_type: [] relation_uri: [] reportno: ~ rev_number: 18 series: ~ source: ~ status_changed: 2011-02-16 19:49:38 subjects: - comp-sci-art-intel - comp-sci-mach-learn succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation type: preprint userid: 10433 volume: ~