Saturday, June 16, 2012

Meta-learning and a Concept Drift

Just some thoughts on how concept drift can appear in the meta-learning study.

When trying to learn how to learn one can formulate this problem as the problem of controlling the learning algorithm (sub-learner) by another learning algorithm (meta-learner). An approach, which emerges just at the very beginning, is to recognize some sort of regimes or modes of the sub-learner, e.g. use operators A and B in mode 1 and operators 2, 3 and 4 in  mode 2. The definition of these modes depends on the sub-learner.

For example if the sub-learner is a neuroevolutionary algorithm the modes may be:
1. Complexify network structure.
2. Simplify network structure.
3. Search the weights space using current or only slightly changing structure.

There may also be combinations of these or some other modes.

To understand in which mode the sub-learning algorithm is performing some set of mode features should be defined. Possible variant is to trace the algorithm's behaviour and recognize modes depending on the behaviour outcomes. So for NE algorithm there's a set of operators, like add/remove connection or change activations and we can log fitness changes involved by these operators for each individual in the population. Averaged influence of the operators on the Ne algorithm performance over several last generations can indicate the current mode.

However the analysis of operators' performance may be affected by the problem at hand and a good meta-learning approach should be able to deal with various problems. So for problem P1 mode 1 can be indicated by the different operators log than for some other problem P2. It is presumed that the meta-learner starts without knowing which problem the sub-learner is to solve and should make decisions on the fly based upon analysis of sub-learner performance.

Thus it is possible to define at least two possible sources of the concept drift for the meta-learning approach:
1. The concept drift due to problems swtiching (the mode1 definition changes  when the problem is changed).
2. The concept drift due to dynamical problem environment (conditions for sub-learner are changing and hence influence it's performance and operators analysis).