The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams

08/20/2015
by   Rocco De Rosa, et al.
0

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. In this paper, 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. We design four variants of our approach of increasing adaptivity. By means of an extensive empirical evaluation against standard nonparametric baselines, we show state-of-the-art results in terms of accuracy versus model size. For the variant that imposes a strict bound on the model size, we show better performance against all other methods measured at the same model size value. Our empirical analysis is complemented by a theoretical performance guarantee which does not rely on any stochastic assumption on the source generating the stream.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

04/11/2016

Active Learning for Online Recognition of Human Activities from Streaming Videos

Recognising human activities from streaming videos poses unique challeng...
08/25/2021

Direct Nonparametric Predictive Inference Classification Trees

Classification is the task of assigning a new instance to one of a set o...
06/11/2021

ExtendedHyperLogLog: Analysis of a new Cardinality Estimator

We discuss the problem of counting distinct elements in a stream. A stre...
07/09/2019

Contextual One-Class Classification in Data Streams

In machine learning, the one-class classification problem occurs when tr...
06/29/2018

Nonparametric learning from Bayesian models with randomized objective functions

Bayesian learning is built on an assumption that the model space contain...
09/03/2015

Incremental Active Opinion Learning Over a Stream of Opinionated Documents

Applications that learn from opinionated documents, like tweets or produ...
11/03/2015

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Big Data streams are being generated in a faster, bigger, and more commo...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.