Identifying the impact of decision variables for nonlinear classification tasks
This paper presents a novel procedure to improve a class of learning systems known as lazy learning algorithms by optimizing the selection of variables and their attendant weights through an artificial neural network and a genetic algorithm. The procedure utilizes its previous knowledge basealso called a case baseto select an effective subset for adaptation. In particular, the procedure explores a space of N variables and generates a reduced space of M dimensions. This is achieved through clustering and compaction. The clustering stage involves the minimization of distances among individuals within the same class while maximizing the distances among different classes. The compaction stage involves the elimination of the irrelevant or redundant feature dimensions. To achieve these two goals concurrently through the evolutionary process, new measures of fitness have been developed. The metrics lead to procedures which exhibit superior characteristics in terms of both accuracy and efficiency. The efficiency springs from a reduction in the number of features required for analysis, thereby saving on computational cost as well as data collection requirements. The utility of the new techniques is validated against a variety of data sets from natural and commercial sources.
lazy learning artificial neural network genetic algorithms feature weighting similarity assessment k-nearest neighbor