Simulated annealing based classification
S. Finnerty, Sandip Sen · 2002
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.>