An Ensemble-Based Incremental Learning Approach to Data Fusion
- 12 March 2007
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Vol. 37 (2), 437-450
- https://doi.org/10.1109/tsmcb.2006.883873
Abstract
This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen dataKeywords
This publication has 71 references indexed in Scilit:
- Speaker recognition—general classifier approaches and data fusion methodsPattern Recognition, 2002
- Sensor fusionAnnual Reviews in Control, 2002
- Decision templates for multiple classifier fusion: an experimental comparisonPattern Recognition, 2001
- Combining multiple classifiers by averaging or by multiplying?Pattern Recognition, 2000
- On combining classifiersIeee Transactions On Pattern Analysis and Machine Intelligence, 1998
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Sensor Fusion for Mobile Robot NavigationProceedings of the IEEE, 1997
- Analysis of decision boundaries in linearly combined neural classifiersPattern Recognition, 1996
- Combining the results of several neural network classifiersNeural Networks, 1994
- Adaptive Mixtures of Local ExpertsNeural Computation, 1991