Evolutionary feature and instance selection for traffic sign recognition

Abstract

The problem of traffic sign recognition is generally approached by first constructing a classifier, which is trained by some relevant image features extracted from traffic signs, to recognize new unknown traffic signs. Feature selection and instance selection are two important data preprocessing steps in data mining, with the former aimed at removing some irrelevant and/or redundant features from a given dataset and the latter at discarding the faulty data. However, there has thus far been no study examining the impact of performing feature and instance selection on traffic sign recognition performance. Given that genetic algorithms (GA) have been widely used for these types of data preprocessing tasks in related studies, we introduce a novel genetic-based biological algorithm (GBA). GBA fits “biological evolution” into the evolutionary process, where the most streamlined process also complies with reasonable rules. In other words, after long-term evolution, organisms find the most efficient way to allocate resources and evolve. Similarly, we closely simulate the natural evolution of an algorithm, to find an option it will be both efficient and effective. Experiments are carried out comparing the performance of the GBA and a GA based on the German Traffic Sign Recognition Benchmark. The results show that the GBA outperforms the GA in terms of the reduction rate, classification accuracy, and computational cost.