Open AccessFeature-Enhanced Data Classification Using B-center Function
Author(s) -
Enyu Li,
Leyu Lyu,
Wenfei Mao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1994/1/012005
Subject(s) - support vector machine , pattern recognition (psychology) , cluster analysis , gaussian function , perceptron , feature vector , artificial intelligence , computer science , kernel (algebra) , linear classifier , feature (linguistics) , weighting , multilayer perceptron , gaussian , function (biology) , dimension (graph theory) , data mining , mathematics , artificial neural network , medicine , linguistics , philosophy , physics , radiology , quantum mechanics , combinatorics , evolutionary biology , biology , pure mathematics
Linear-kernel Support Vector Machine (SVM) classification algorithm needs to constantly calculate the similarity between the data to be tested and the sample during classification. If samples in the dataset are linearly indivisible, the accuracy of the algorithm will get worse. The feature weight calculation based on category information in Multi-layer Perceptron (MLP) algorithm is not accurate enough to express the relationship between features and categories, i.e. it cannot distinguish categories precisely when the features with the same category frequency. In order to improve the performance of MLP and linear-kernel SVM algorithms in classification, an improved function: the Base function, which is based on clustering, is proposed in this paper. Before running the classification algorithm, a series of possible centers is predicted, after which the Base function is used for data clustering to increase the dimension of data, and then samples with more features are passed to classification models. In the high-position Gaussian data space, the Base function can obviously improve the spatial distribution of samples. Experimental comparison in the thesis also proved that the Base function can improve the performance of MLP or SVM in classification effectively. The proposed feature-enhanced MLP is superior to the baseline with 32.6% accuracy promotion on the Gaussian dataset.