Can AdaBoost.M1 learn incrementally? A comparison to learn++ under different combination rules

Hussein Syed Mohammed; James Leander; Matthew Marbach; Robi Polikar

Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules

Hussein Syed Mohammed, James Leander, Matthew Marbach, Robi Polikar

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We had previously introduced Learn⁺⁺, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn⁺⁺ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.

Original language	English (US)
Title of host publication	Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
Publisher	Springer Verlag
Pages	254-263
Number of pages	10
ISBN (Print)	3540386254, 9783540386254
State	Published - Jan 1 2006
Event	16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece Duration: Sep 10 2006 → Sep 14 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4131 LNCS - I
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	16th International Conference on Artificial Neural Networks, ICANN 2006
Country/Territory	Greece
City	Athens
Period	9/10/06 → 9/14/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Cite this

Mohammed, H. S., Leander, J., Marbach, M., & Polikar, R. (2006). Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings (pp. 254-263). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4131 LNCS - I). Springer Verlag.

Mohammed, Hussein Syed ; Leander, James ; Marbach, Matthew et al. / Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules. Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings. Springer Verlag, 2006. pp. 254-263 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.",

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Mohammed, HS, Leander, J, Marbach, M & Polikar, R 2006, Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules. in Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4131 LNCS - I, Springer Verlag, pp. 254-263, 16th International Conference on Artificial Neural Networks, ICANN 2006, Athens, Greece, 9/10/06.

Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules. / Mohammed, Hussein Syed; Leander, James; Marbach, Matthew et al.
Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings. Springer Verlag, 2006. p. 254-263 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4131 LNCS - I).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.

AB - We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.

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Mohammed HS, Leander J, Marbach M, Polikar R. Can AdaBoost.M1 learn incrementally? A comparison to learn⁺⁺ under different combination rules. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings. Springer Verlag. 2006. p. 254-263. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).