Sunday, December 4, 2016

Concept Profiling Framework for Recurring Drifts in Data Streams

This paper was published in Australasian Conference on Artificial Intelligence 2016, 2016 by Robert Anderson, Yun Sing Koh, Gillian Dobbie. Attached is the abstract and a brief summary of the paper.


We propose the Concept Profiling Framework (CPF), a meta-learner that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour. We introduce a memory-efficient version of our framework and show that it can operate faster and with less memory than a naive implementation while achieving similar accuracy. We compare this memory-efficient version of CPF to a state-of-the-art meta-learner made to handle recurrent drift and show that we can regularly achieve improved classification accuracy along with runtime and memory use. We provide results from testing on synthetic and real-world datasets to prove CPF's value in classifying data streams with recurrent concepts.


We present the Concept Profiling Framework (CPF). This is a meta-learning approach that maintains a collection of classifiers and uses a drift detector. When our drift detector indicates a drift state i.e. that our current classifier is no longer suitable, we check our collection of classifiers for one better suited to the current stream. If one meets a set level of accuracy, we will select it as the current classifier; otherwise a new classifier is produced and trained on recent data. If this new classifier behaves similarly to a classifier in our collection, we will choose that existing classifier as our current model; otherwise we will add the new classifier to the collection and use that as our current classifier.

We introduce two techniques to allow efficient handling of recurrent concepts. First, we regularly compare behaviour of our classifiers, and over time, our certainty about their similarity will improve. If they behave similarly, we can use the older model to represent the newer one. Second, we implement a fading mechanism to constrain the number of models, a points-based system that retains models that are recent or frequently used. Through observing reuse patterns, we can understand how patterns recur in our stream.

The figure above  describes the framework in further detail. We use a meta-learning framework with a collection of one or more incremental classifiers. One is designated as our current classifier. A drift detector signals warning and drift states. On a warning state, the meta-learner will stop training the current classifier and store instances from the data stream in a buffer. If a drift state follows, the meta-learner looks for an existing model in the collection that classifies the warning buffer accurately to use as the current classifier. If it cannot find one, it will create a new model trained on even buffer instances. When an existing model behaves similarly to this new model (when tested on odd buffer instances) that model will be reused; otherwise the new model is trained on odd buffer instances and used. Every model in the collection is tested on the buffer, and the results will be compared and stored. Where it is found that models classify similarly to one another, the older model will represent the newer one.

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