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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2021,12,15]]},"abstract":"<jats:p> A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a \u201cstacking with ridge regression\u201d strategy is applied to the fusion block to get the final output of MFBLS. 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