Abstract
In this introduction, we define the termbias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized.
Article PDF
Similar content being viewed by others
References
Aha, D. and Bankert, R. (1994). Feature selection for case-based classification of cloud types. InProceedings of the 1994 Workshop on Case-Based Reasoning, pages 106–112. AAAI Press.
Almuallim, H. and Dietterich, T. (1991). Learning with many irrelevant features. InProceedings of the Ninth National Conference on Artificial Intelligence, pages 547–552. AAAI Press.
Angluin, D. (1988). Queries and concept learning.Machine Learning, 2(4):319–342.
Baltes, J. and MacDonald, B. (1992). Case-based meta learning: Sustained learning supported by a dynamically biased version space. InProceedings of the ML92 Workshop on Biases in Inductive Learning.
Bloedorn, E., Michalski, R., and Wnek, J. (1993). Multistrategy constructive induction: AQ17-MCI. InProceedings of the Second International Workshop on Multistrategy Learning, pages 188–206.
Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. (1987). Occam's razor.Information Processing Letters, 24:377–380.
Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. (1989). Learnability and the Vapnik-Chervonenkis dimension.Journal of the Association for Computing Machinery, 36(4):929–965.
Cardie, C. (1993). Using cognitive biases to guide feature set selection. InProceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pages 743–748. Lawrence Erlbaum Associates.
Chaitin, G. J. (1977). Algorithmic information theory.IBM J. Res. Develop., 21:350–359.
Chrisman, L. (1989). Evaluating bias during PAC-learning. InMachine Learning Workshop, pages 469–471. Morgan Kaufmann.
Cobb, H. (1992). Inductive biases in a reinforcement learner. InProceedings of the ML92 Workshop on Biases in Inductive Learning.
Cohen, W. W. (1995). Grammatically biased learning: Learning Horn theories using an explicit antecedent description language.Artificial Intelligence.
Datta, P. and Kibler, D. (1992). Utilizing prior concepts for learning. InProceedings of the ML92 Workshop on Biases in Inductive Learning.
desJardins, M. (1994). Evaluation of learning biases using probabilistic domain knowledge. InComputational Learning Theory and Natural Learning Systems, vol. 2, chapter 7, pages 95–112. The MIT Press.
Gordon, D. (1990).Active Bias Selection for Incremental, Supervised Concept Learning. PhD thesis, University of Maryland, Department of Computer Science. Also available as Technical Report UMIACS-TR-90-60 CS-TR-2464.
Gordon, D. and Subramanian, D. (1993). A multistrategy learning scheme for agent knowledge acquisition.Informatica, 17:331–346.
Hirsh, H. (1990). Knowledge as bias. InChange of Representation and Inductive Bias. Kluwer Academic Publishers.
John, G., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. InProceedings of the Eleventh International Conference on Machine Learning, pages 121–129. Morgan Kaufmann.
Kietz, J. and Morik, K. (1994). A polynomial approach to the constructive induction of structured knowledge.Machine Learning, 14(2):193–218.
Kira, K. and Rendell, L. (1992). A practical approach to feature selection. InProceedings of the Ninth International Conference on Machine Learning, pages 249–256. Morgan Kaufmann.
Matheus, C. (1991). The need for constructive induction. InProceedings of the Eighth International Workshop on Machine Learning, pages 173–177. Tioga.
Michalski, R. (1983). A theory and methodology of inductive learning. In Michalski, R., Carbonell, J., and Mitchell, T., editors,Machine Learning I, pages 83–134. Tioga.
Mitchell, T. (1980). The need for biases in learning generalizations. Technical Report CBM-TR-117, Rutgers University.
Pratt, L. Y. (1993). Discriminability-based transfer between neural networks. In Giles, C. L., Hanson, S. J., and Cowan, J. D., editors,Advances in Neural Information Processing Systems 5, pages 204–211. Morgan Kaufmann Publishers, San Mateo, CA.
Provost, F. J. and Buchanan, B. (1992). Inductive policy. InAAAI-92, pages 255–261. AAAI Press/The MIT Press.
Rendell, L. (1987). Similarity-based learning and its extensions.Computational Intelligence, 3:241–266.
Rendell, L. (1990). Feature construction for concept learning. InChange of Representation and Inductive Bias, pages 327–353. Kluwer.
Rendell, L., Seshu, R., and Tcheng, D. (1987). More robust concept learning using dynamically-variable bias. InProceedings of the Fourth International Workshop on Machine Learning, pages 66–78. Morgan Kaufmann.
Russell, S. and Grosof, B. (1987). A declarative approach to bias in concept learning. InAAAI, pages 505–510.
Saxena, S. (1991). On the effect of instance representation on generalization. InProceedings of the Eighth International Workshop on Machine Learning, pages 198–202. Morgan Kaufmann.
Schaffer, C. (1994). A conservation law for generalization performance. InProceedings of the Eleventh International Conference on Machine Learning, pages 259–265. Morgan Kaufmann.
Schultz, A. and Grefenstette, J. (1990). Improving tactical plans with genetic algorithms. InProceedings of the IEEE Conference on Tools for AI, pages 328–334. IEEE Press.
Solomonoff, R. J. (1964). A formal theory of inductive inference.Information and Control, 7.
Spears, W. and Gordon, D. (1992). Is consistency harmful? InProceedings of the ML92 Workshop on Biases in Inductive Learning.
Towell, G., Shavlik, J., and Noordewier, M. (1990). Refinement of approximate domain theories by knowledge-based neural networks. InProceedings of AAAI-90, pages 861–866. Morgan Kaufmann.
Utgoff, P. (1986). Shift of bias for inductive concept learning. In Michalski, R., Carbonell, J., and Mitchell, T., editors,Machine Learning II, pages 107–148. Morgan Kaufman.
Vafaie, H. and Jong, K. D. (1993). Robust feature selection algorithms. InProceedings of the Fifth Conference on Tools for Artificial Intelligence, pages 356–363. IEEE Computer Society Press.
Vapnik, V. and Chervonenkis, A. (1971). On the uniform convergence of relative frequencies of events to their probabilities.Theory of Probability and its Applications, 16(2):264–280.
Wnek, J. and Michalski, R. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments.Machine Learning, 14(2):139–168.
Wolpert, D. (1992). On the connection between in-sample testing and generalization error.Complex Systems, 6:47–94.
Wrobel, S. (1994). Concept formation during interactive theory revision.Machine Learning, 14(2):169–192.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Gordon, D.F., Desjardins, M. Evaluation and selection of biases in machine learning. Mach Learn 20, 5–22 (1995). https://doi.org/10.1007/BF00993472
Issue Date:
DOI: https://doi.org/10.1007/BF00993472