Adaptive hierarchical space partitioning for online classification

Date
2016
Advisor
Instructor
Source Title
Proceedings of the 24th European Signal Processing Conference, EUSIPCO 2016
Print ISSN
2219-5491
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
2290 - 2294
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.

Course
Other identifiers
Book Title
Keywords
Artificial intelligence, Learning algorithms, Learning systems, Classification methods, Classifier models, Ensemble techniques, On-line algorithms, On-line classification, Performance guarantees, Space partitioning, State of the art, Signal processing
Citation
Published Version (Please cite this version)