- (Aha et al., 1991) ⇒ David W. Aha, Dennis Kibler, and Marc K. Albert. (1991). “Instance-based Learning Algorithms.” In: Machine Learning, 6(1). doi:10.1023/A:1022689900470.
- ~2466 …
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.
- David W. Aha. (1989a). “Incremental, Instance-based Learning of Independent and Graded Concept Descriptions.” In: Proceedings of the Sixth International Workshop on Machine Learning (pp. 387–391). Ithaca, NY: Morgan Kaufmann.
- David W. Aha. (1989b). “Incremental Learning of Independent, Overlapping, and Graded Concepts with an Instance-based Process Framework (Technical Report 89–10).” Irvine, CA: University of California, Irvine, Department of Information and Computer Science.
- David W. Aha. (1989c). “Tolerating Noise, Irrelevant Attributes, and Novel Attributes in Instance-based Learning Algorithms.” In: Proceedings of the IJCAI-1989 Workshop on Symbolic Problem Solving in Noisy, Novel, and Uncertain Task Environments.
- David W. Aha, and Dennis Kibler. (1989). “Noise-tolerant Instance-based Learning Algorithms.” In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI 1989).
- BareissE.R., PorterB., & WierC.C. (1987). PROTOS: An exemplar-based learning apprentice. Proceedings of the Fourth International Workshop on Machine Learning (pp. 12–23). Irvine, CA: Morgan Kaufmann.
- BarsalouL.W. (1983). Ad hoc categories. Memory and Cognition, 11, 211–227.
- BlumerA., EhrenfeuchtA., HausslerD., & WarmuthM. (1986). Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension. Proceedings of the Eighteenth Annual Association for Computing Machinery Symposium on Theory of Computing (pp. 273–282). Berkeley, CA: Association for Computing Machinery.
- BreimanL., Jerome H. Friedman, OlshenR.A., & StoneC.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group.
- BradshawG. (1987). Learning about speech sounds: The NEXUS project. Proceedings of the Fourth International Workshop on Machine Learning (pp. 1–11). Irvine, CA: Morgan Kaufmann.
- BrooksL. (1978). Nonanalytic concept formation and memory for instances. In E.Rosch & B.B.Lloyd (Eds.), Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.
- BrooksL. (1989). Concept formation and particularizing learning. In S.Hanson & R.Davis (Eds.), Information, language, & cognition: Vancouver studies in cognitive science (Vol. 1). Vancouver, B.C.: UBC Press.
- CestnikB., KononenkoI., & BratkoI. (1987). ASSISTANT-86: A knowledge-elicitation tool for sophisticated users. In I.Bratko & N.Lavrac (Eds.), Progress in machine learning. Bled, Yugoslavia: Sigma Press.
- ClarkP.E. (1989). Exemplar-based reasoning in geological prospect appraisal (Technical Report 89–034). Glasgow, Scotland: Turing Institute.
- ClarkP.E., & NiblettT. (1989). The CN2 induction algorithm. Machine Learning, 3, 261–284.
- CoverT.M., & HartP.E. (1967). Nearest neighbor pattern classification. Institute of Electrical and Electronics Engineers Transactions on Information Theory, 13, 21–27.
- DasarathyB.V. (1980). Nosing around the neighborhood: A new system structure and classification rule for recognition in partially exposed environments. Pattern Analysis and Machine Intelligence]], 2, 67–71.
- Detrano, R., M.D. (1988). International application of a new probability algorithm for the diagnosis of coronary artery disease. Unpublished manuscript.
- DietterichT.G., & MichalskiR.S. (1983). A comparative review of selected methods for learning from examples. In R.S.Michalski, J.G.Carbonell, & Tom M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Mateo, CA: Morgan Kaufmann.
- FisherD.F. (1989). Noise-tolerant concept clustering. Proceedings of the Eleventh International Conference on Artificial Intelligence (pp. 825–830). Detroit, MI: Morgan Kaufmann.
- Gates, G.W. (1972). The reduced nearest neighbor rule. IEEE Transactions on Information Theory, 431–433.
- HartP.E. (1968). The condensed nearest neighbor rule. Institute of Electrical and Electronics Engineers and Transactions on Information Theory, 14, 515–516.
- HintzmanD.L. (1986). ldquoSchema abstractionrdquo in a multiple-trace memory model. Psychological Review, 93, 411–428.
- HoggR.V., & TanisE.A. (1983). Probability and statistical inference. New York, NY: Macmillan.
- Jabbour, K., Riveros, J.F.V., Landsbergen, D., & Meyer, W. (1987). ALFA: Automated load forecasting assistant. Proceedings of the 1987 IEEE Power Engineering Society Summer Meeting. San Francisco, CA.
- Kibler, D., & Aha, D.W. (1988). Case-based classification. Proceedings of the Case-based Reasoning Workshop at AAAI 1988 (pp. 62–67). Unpublished manuscript.
- KiblerD., & AhaD.W. (1989). Comparing instance-saving with instance-averaging learning algorithms. In D.P.Benjamin (Ed.), Change of representation and inductive bias. Norwell, MA: Kluwer Academic Publishers.
- KiblerD., AhaD.W., & AlbertM. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, 5, 51–57.
- KotonP. (1988). Reasoning about evidence in causal explanations. Proceedings of the Seventh National Conference on Artificial Intelligence (pp. 256–261). St. Paul, MN: Morgan Kaufmann.
- MarkovitchS., & ScottP.D. (1989). Information filters and their implementation in the SYLLOG system. Proceedings of the Sixth International Workshop on Machine Learning (pp. 404–407). Ithaca, NY: Morgan Kaufmann.
- MedinD.L., & SchafferM.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
- MichalskiR.S., & LarsonJ.B. (1978). Selection of most representative training examples and incremental generation of VL 1 hypotheses: The underlying methodology and the description of programs ESEL and AQ11 (Technical Report 867). Urbana, IL: University of Illinois, Department of Computer Science.
- MichalskiR.S., MozeticI., HongJ., & LavraccaronN. (1986). The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 1041–1045). Philadelphia, PA: Morgan Kaufmann.
- Michie, D., Muggleton, S., Riese, C., & Zubrick, S. (1984). Rulemaster: A second-generation knowledge-engineering facility. 1984 Conference on Artificial Intelligence and Applications.
- NosofskyR.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 15, 39–57.
- J. Ross Quinlan. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
- J. Ross Quinlan. (1987). Generating production rules from decision trees. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 304–307). Milan, Italy: Morgan Kaufmann.
- J. Ross Quinlan. (1988). An empirical comparison of genetic and decision-tree classifiers. Proceedings of the Fifth International Conference on Machine Learning (pp. 135–141). Ann Arbor, MI: Morgan Kaufmann.
- J. Ross Quinlan, Compton, P.J., Horn, K.A., & Lazurus, L. (1986). Inductive knowledge acquisition: A case study. Proceedings of the Second Australian Conference on Applications of Expert Systems. Sydney, Australia.
- RendellL. (1988). Learning hard concepts. Proceedings of the Third European Working Session on Learning (pp. 177–200). Glasgow, Scotland: Pitman Publishing.
- RisslandE.L., KolodnerJ., & WaltzD. (1989). Case-based reasoning from DARPA: Machine learning program plan. Proceedings of the Case-based Reasoning Workshop (pp. 1–13). Pensecola Beach, FL: Morgan Kaufmann.
- RosenblattF. (1962). Principles of neurodynamics. New York, NY: Spartan.
- RumelhartD.E., McClellandJ.L., & The PDP Research Group (Eds.) (1987). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1). Cambridge, MA: MIT Press.
- Shamos, M.I., & Hoey, D. (1975). Closest point problems. Proceedings of the Sixteenth Annual Institute of Electrical and Electronic Engineers Symposium on the Foundations of Computer Science (pp. 151–162.) IEEE Computer Society.
- SalzbergS. (1988). Exemplar-based learning: Theory and implementation (Technical Report TR-10–88). Cambridge, MA: Harvard University, Center for Research in Computing Technology.
- SchlimmerJ.C., & FisherD. (1986). A case study of incremental concept induction. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496–501). Philadelphia, PA: Morgan Kaufmann.
- SmithE.E., & MedinD.L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.
- StanfillC., & WaltzD. (1986). Toward memory-based reasoning. Communications of the ACM, 29, 1213–1228.
- UtgoffP.E. (1989). Incremental induction of decision trees. Machine Learning, 4, 161–186.
- ValiantL.G. (1984). A theory of the learnable. Communications of the Association for Computing Machinery, 27, 1134–1142.
- Van deVeldeW. (1989). IDL, or taming the multiplexer. Proceedings of the Fourth European Working Session on Learning (pp. 211–225). Montpellier, France: Morgan Kaufmann.
- VolperD.J., & HampsonS.E. (1987). Learning and using specific instances. Biological Cybernetics, 57, 57–71.,
|1991 InstanceBasedLearning||David W. Aha|
Marc K. Albert
|Instance-based Learning Algorithms||Machine Learning Subject Area||http://sci2s.ugr.es/keel/pdf/algorithm/articulo/aha1991.pdf||10.1023/A:1022689900470||1991|