An evolutionary classification method based on fireworks algorithm
by Yu Xue; Binping Zhao; Tinghuai Ma; Alex X. Liu
International Journal of Bio-Inspired Computation (IJBIC), Vol. 11, No. 3, 2018

Abstract: Classification and clustering are two important problems in machine learning and data mining. Recently, many evolutionary computation (EC) techniques have been employed to solve the clustering problems by taking the advantage of the global search ability of EC. Different from the situation in clustering research issues, EC techniques are only employed to improve the performance of the classifiers either by optimising their parameters and structures, or by pre-processing their inputs. In this paper, we propose an evolutionary optimisation classification model for the classification problems and the fireworks algorithm (FWA) is employed to solve the classification model directly. In the optimisation classification model, a linear equation set is constructed based on the training sets and an objective function which can be optimised by the EC techniques is proposed according to the equation set. Ten different datasets have been employed in the experiments. For each dataset, 70% instances are used as a training set while the rest are used as a test set. The experiment results show that it is possible to directly solve the classification problems by EC techniques through introducing the evolutionary optimisation classification model. Moreover, FWA performs better than particle swarm optimisation (PSO) algorithm on most datasets.

Online publication date: Mon, 14-May-2018

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