Adaptive artificial datasets through learning classifier systems for classification tasks
In producing an artificial dataset, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problems difficulty. If humans can set up the difficulty levels appropriately, then learning systems can solve classification tasks successfully. This paper introduces an autonomous classification problem generation approach. The problems difficulty is adapted based on the classification agents performance within the defined attributes. An automated problem generator has been created to evolve simulated datasets whilst the classification agent, in this case a learning classifier system (LCS), attempts to learn the evolving datasets. The idea here is to tune the problems difficulty autonomously such that the problems characteristics may be determined effectively. Furthermore, this framework can empirically test the learning bounds of the classification agent whilst lowering human involvement. Initially, tabu search was integrated in the problem generator to discover the best combination of domain features in order to adjust the problems difficulty. In order to overcome stagnation in local optimum, a Pittsburgh-style LCSs, A-PLUS, was adapted for the first time to the problem generator. In this way, the effect of the problems characteristics, e.g. noise, which alter the classification agents performance, becomes human readable. Experiments confirm that the problem generator was able to tune the problems difficulty either to make the problem harder or easier so that it can either increase or decrease the classification agents performance.
artificial dataset learning classifier system pattern classification