Abstract
In this paper we argue that parallel and/or distributed compute resources can be used differently: instead of focusing on speeding up algorithms, we propose to focus on improving accuracy. In a nutshell, the goal is to tune data mining algorithms to produce better results in the same time rather than producing similar results a lot faster. We discuss a number of generic ways of tuning data mining algorithms and elaborate on two prominent examples in more detail. A series of exemplary experiments is used to illustrate the effect such use of parallel resources can have.
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References
Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press (1972)
Johnson, D.S.: Approximation algorithms for combinatorial problems. In: Proceedings of the Fifth Annual ACM Symposium on Theory of Computing, STOC 1973, pp. 38–49. ACM, New York (1973)
Beasley, J.E.: Or-library: Distributing test problems by electronic mail. The Journal of the Operational Research Society 41(11), 1069–1072 (1990)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Quinlan, J.R., Cameron-Jones, R.M.: Oversearching and layered search in empirical learning. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1019–1024 (1995)
Murthy, S., Salzberg, S.: Lookahead and pathology in decision tree induction. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1025–1031 (1995)
Iba, W., Langley, P.: Induction of one-level decision trees. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 233–240 (1992)
Sarkar, U., Chakrabarti, P., Ghose, S., Desarkar, S.: Improving Greedy Algorithms by Lookahead-Search. Journal of Algorithms 16(1), 1–23 (1994)
Elomaa, T., Malinen, T.: On Lookahead Heuristics in Decision Tree Learning. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 445–453. Springer, Heidelberg (2003)
Esmeir, S., Markovitch, S.: Lookahead-based algorithms for anytime induction of decision trees. In: Twenty-First International Conference on Machine Learning, ICML 2004, pp. 33–40. ACM Press, New York (2004)
Brönnimann, H., Goodrich, M.T.: Almost optimal set covers in finite vc-dimension (preliminary version). In: Proceedings of the Tenth Annual Symposium on Computational Geometry, SCG 1994, pp. 293–302. ACM, New York (1994)
Berger, B., Rompel, J., Shor, P.W.: Efficient nc algorithms for set cover with applications to learning and geometry. Journal of Computer and System Sciences 49(3), 454–477 (1994)
Blelloch, G.E., Peng, R., Tangwongsan, K.: Linear-work greedy parallel approximate set cover and variants. In: Proceedings of the 23rd ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2011, pp. 23–32. ACM, New York (2011)
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Akbar, Z., Ivanova, V.N., Berthold, M.R. (2012). Parallel Data Mining Revisited. Better, Not Faster. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_4
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DOI: https://doi.org/10.1007/978-3-642-34156-4_4
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