An accelerator for attribute reduction based on perspective of objects and attributes
Feature selection is an active area of research in pattern recognition, machine learning and artificial intelligence, which greatly improves the performance of forecasting or classification. In rough set theory, attribute reduction, as a special form of feature selection, aims to retain the discernability of the original attribute set. To solve this problem, many heuristic attribute reduction algorithms have been proposed in the literature. However, these methods are computationally time-consuming for large scale datasets. Recently, an accelerator was introduced by computing reducts on gradually reducing the size of the universe. Although the accelerator can considerably shorten the computational time, it remains a challenging issue. To further enhance the efficiency of these algorithms, we develop a new accelerator for attribute reduction, which simultaneously reduces the size of the universe and the number of attributes at each iteration of the process of reduction. Based on the new accelerator, several representative heuristic attribute reduction algorithms are accelerated. Experiments show that these accelerated algorithms can significantly reduce computational time while maintaining their results the same as before.
attribute reduction feature selection rough set accelerating algorithm large scale data