Parallel computing of fuzzy integrals: Performance and test

Abstract

Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor.