Description:

Incremental (Online) Nonparametric Classifier. You can classify both points (standard) or matrices (multivariate time series or any set of features (local features in computer vision field)).

Abstract. Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data stream is arbitrarily long), and be nonparametric in order to achieve high accuracy even in complex and dynamic environments. Moreover, the learning system must be parameterless ---traditional tuning methods are problematic in streaming settings--- and avoid requiring prior knowledge of the number of distinct class labels occurring in the stream.

We introduce a new algorithmic approach for nonparametric learning in data streams. Our approach addresses all above mentioned challenges by learning a model that covers the input space using simple local classifiers. The distribution of these classifiers dynamically adapts to the local (unknown) complexity of the classification problem, thus achieving a good balance between model complexity and predictive accuracy.

Please Cite :

If you use Incremental Multivariate Time Series (set of local features) Classification:

@inproceedings{de2014online, title={Online action recognition via nonparametric incremental learning}, author={De Rosa, Rocco and Cesa-Bianchi, Nicol{`o} and Gori, Ilaria and Cuzzolin, Fabio}, booktitle={Submitted to British Machine Vision Conference (BMVC 2014)}, year={2014} }

If you use Incremental (Online) Classification:

@inproceedings{derosa2015abacoc, title={The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams}, author={De Rosa, Rocco and Orabona, Francesco and Cesa-Bianchi, Nicolo}, booktitle={Data Mining (ICDM), 2015 IEEE International Conference on}, year={2015}, organization={IEEE} }

NEW!!NEW!! C++ version at: https://github.com/ilaria-gori/ABACOC

Changes to previous version:

version 2: parameterless system, constant model size, prediction confidence (for active learning).

NEW!! C++ version at: https://github.com/ilaria-gori/ABACOC

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows

Data Formats: Matlab, Java

Tags: Active Learning, Online Learning, Action Recognition, Multivariate Time Series Classification, Temporal Classification, Human Activity Recognition, Constant Model Size, Incremental Classifier, Real Ti

Archive: download here

Other available revisons

Version	Changelog	Date
2.0	version 2: parameterless system, constant model size, prediction confidence (for active learning). NEW!! C++ version at: https://github.com/ilaria-gori/ABACOC	May 29, 2015, 11:57:28
1.0	Initial release of the library, future changes will be advertised shortly.	July 14, 2014, 16:27:03

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