Simulated annealing based classification

Attribute based classification has been one of the most active areas of machine learning research over the past decade. We view the problem of hypotheses formation for classification as a search problem. Whereas previous research acquiring classification knowledge have used a deterministic bias for forming generalizations, we use a more random bias for taking inductive leaps. We re-formulate the supervised classification problem as a function optimization problem, the goal of which is to search for a hypotheses that minimizes the number of incorrect classifications of training instances. We use a simulated annealing based classifier (SAC) to optimize the hypotheses used for classification. The particular variation of simulated annealing algorithm that we have used is known as Very Fast Simulated Re-annealing (VFSR). We use a batch-incremental mode of learning to compare SAC with a genetic algorithm based classifier, GABIL, and a traditional incremental machine learning algorithm, ID5R. By using a set of artificial target concepts, we show that SAC performs better on more complex target concepts