A Synapse Plasticity Model for Conceptual Drift Problems

A Synapse Plasticity Model for Conceptual Drift Problems Rudolph L Mappus IV (cmappus@cc.gatech.edu) Ashwin Ram (ashwin@cc.gatech.edu) College of Computing, Georgia Institute of Technology Atlanta, GA 30332 USA the component such as the RPM of component shafts or bearing temperatures. The goal of the learner in this domain is to classify the working state of the component in real time, given a stream of samples from the sensor array. Suppose that during the daytime hours, bear- ing temperatures rise from ambient heat, to levels that would, under other conditions, raise warning. In this case, the previous values used to classify nominal oper- ation do not reflect the new operating parameters. A supervised learner that is not modified after training re- quires retraining on the new instances. We are interested in applying biologically plausible computational models to this problem. Abstract Traditional supervised learning techniques do not ad- dress online learning problems such as concept drift, due to the fact that learning is offline when using these meth- ods. Associative neural networks using Hebbian learning rules show robust performance in classification tasks in- volving concept drift. Biologically plausible neural net- works represent a set of computational models designed to be more strongly related to biological neuron models. In this paper, we apply a biologically inspired plasticity model of synapse dynamics to a concept drift classifi- cation problem. The motivation for this method is to provide more biologically plausible networks in cognitive tasks. Introduction Concept Drift Concept drift is a real world problem associated with concept learning and classification: over time or as an agent gains more information about a concept and re- lated concepts, the parameters of the features defining the concept change. Traditional supervised learners do not address the issue. Learning in problem domains with static concepts also avoids the problem, but it is a prob- lem in dynamic learning problems and agents. Intuitively, neural networks seem to have properties that would be helpful in concept drift problems. Partic- ularly, associative neural networks seem appropriate to the problem. Traditional artificial neural networks how- ever were designed as computational models first based on observed neuron behavior second. Previous Work Computational modeling of neuron processes has re- ceived a wealth of research (O’Reilly and Munakata, 2000). Some well researched models have been ap- plied to learning problems such as concept drift. Biehl and Schwarze (Biehl and Schwarze, 1993) demonstrate a Hebbian learning model for handling random as well as correlated concept drift. Widmer and Kubat (Widmer and Kubat, 1996) show how latent variables can influ- ence concept drift in the form of context. In this paper, we show results of timing dependent synapses, whose po- tentiation is governed by firing rate patterns of pre- and post-synaptic neurons. Schlimmer and Granger (Schlim- mer and Granger, 1986) show how learning incrementally from noisy data can still generate classification results. Consider the real world problem of classifying the working state of a power plant component (e.g. a tur- bine generator), given a set of sensor array readings for For the purpose of this discussion, we define concep- tual drift to be the change in distribution parameters that define a classification. For example, suppose that the probability density of the an instance belonging to a classification was defined as Gaussian. The parameters defining that distribution would change over time, indi- cating a conceptual drift. This drift can be correlated, so that concepts at time t are related to concepts at time t + n. Clearly, even in a binary decision task, if drift oc- curs, the decision boundary generated for the concepts at t would not be the decision boundary generated at t + n. Concept drift can increase without repetition (i.e. never returning to a previous distribution). Essentially, a concept’s change is continual, and while the rate of change can slow, the change always leads to a new pa- rameterization of the concept probability distribution in feature space. A good example of this might be bear- ing temperature whose value distribution permanently changes after seating. Repeating and cyclic concept patterns There are some special cases worth mentioning. Concept drift can repeat. Suppose that a concept, after initial training, is explained by a parameterization b 1 . In online learn- ing, more instances arrive, inducing parameterization b 2 . Still more instances arrive, driving the concept param- eterization back to b 1 . The learner, having learned pa- rameterization b 1 would do best to take advantage of the previously learned concept. Concept drift can also be cyclic, where a concept repeats a set of parameteriza- tions in an order, and this ordering repeats. In general, these special cases point to the fact that a latent variable