Abstract
In this paper, we propose a particle swarm optimization method incorporating quantum qubit operation to construct and optimize fuzzy rule-based classifiers. The proposed algorithm, denoted as QiQPSO, is inspired by the quantum computing principles. It employs quantum rotation gates to update the probability of each qubit with the corresponding quantum angle updating according to the update equation of the quantum-behaved particle swarm optimization (QPSO). After description of the principle of QiQPSO, we show how to apply QiQPSO to establish a fuzzy classifier through two procedures. The QiQPSO algorithm is first used to construct the initial fuzzy classification system based on the sample data and the grid method of partitioning the feature space, and then the fuzzy rule base of the initial fuzzy classifier is optimized further by QiQPSO in order to reduce the number of the fuzzy rules and thus improve its interpretability. In order to verify the effectiveness of the proposed method, QiQPSO is tested on various real-world classification problems. The experimental results show that the QiQPSO is able to effectively select feature variables and fuzzy rules of the fuzzy classifiers with high classification accuracies. The performance comparison with other methods also shows that the fuzzy classifier optimized by QiQPSO has higher interpretability as well as comparable or even better classification accuracies.
Highlights
Pattern classification, as one of the most important problems in the field of pattern recognition and machine learning, attempts to assign each input value to one of a given set of classes [1]
Comparing to the initial fuzzy classification system, the one with the fuzzy rule base optimized by quantum-inspired QPSO (QiQPSO) can classify the sample data with an accuracy of 84.11% the same as the initial fuzzy classification system, but the optimized classifier only has 17 fuzzy rules, which means that the interpretability of the classifier is enhanced by QiQPSO
After being optimized by QiQPSO, the initial fuzzy classifier only needs 10 features, and the feature space of each variable is partitioned by 3 Gaussian membership functions. us, the initial fuzzy classifier established by the algorithm has 310 59049 fuzzy rules
Summary
As one of the most important problems in the field of pattern recognition and machine learning, attempts to assign each input value to one of a given set of classes [1]. It involves design of a classifier and the use of the classifier to classify the given data. The use of fuzzy rules for classification is known to be a good method for classification of knowledge representation, which is similar to human knowledge expression, so that it leads to efficient, transparent, and interpretable fuzzy classifiers [3]. During the past few decades, fuzzy classification has been widely used in many fields, including speech recognition, character recognition, text classification, image processing, and weather forecast, to name only a few [4,5,6,7,8,9]
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