Information and knowing when to forget it

In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps unintuitively) benefit from the latter too. We demonstrate that if forgetting is implemented in a purposeful and date driven manner, there are a number of benefits which can be gained from discarding information. The framework we introduce is a general one and can be used with any baseline predictor of choice. Hence in this sense it is best described as a meta-algorithm. The method we described was developed through a series of steps which increase the adaptability of the model, while being data driven.We first discussed a weakly adaptive forgetting process which we termed passive forgetting. A fully adaptive framework, which we termed active forgetting was developed by enveloping a passive forgetting process with a monitoring, self-aware module which detects contextual changes and makes a statistically informed choice when the model parameters should be abruptly rather than gradually updated. The effectiveness of the proposed metaframework was demonstrated on a real world data set concerned with a challenge of major practical importance: that of predicting currency exchange rates. Our approach was shown to be highly effective, reducing prediction errors by nearly 40%.