In this paper, we investigate a method of performing feature transformation on input data in a 1-dimensional space in order to increase the accuracy of classifiers. Through optimized feature transformation, it is possible to create data which generate the models with high accuracy than the original data. We use Genetic Programming (GP) to find a feature transformation function. We proposed evaluation functions using GP and have been successful in finding transformation functions with a high degree of accuracy. On the other hand, where there is a deviation in the number of data items belonging to multiple classes, classes with a large number of data items are more accurate than those that do not. In order to resolve this, referring to existing research, we examined a method of handling the problem of improving accuracy and correcting class imbalanced accuracy from the generated models based on multi-purpose optimization. We then investigated the method of multi-purpose optimization and how to determine the threshold for classification. The results of the investigation were that we could obtain a transformation function that was more accurate and could consider the accuracy of multiple classes simultaneously.