An improved deep forest classification algorithm Online publication date: Wed, 14-Sep-2022
by Jiaman Ding; Cuihua Liu; Runxin Li; Jinguo You; Lianyin Jia
International Journal of Modelling, Identification and Control (IJMIC), Vol. 40, No. 4, 2022
Abstract: Recent studies suggest that deep forest is also a kind of deep learning while overcoming the problems of excessive parameters and hard-to-adjust parameters in deep neural networks. However, the deep forest does not consider the classification contributions of each forest or the time and memory consumption caused by high-dimensional features, thus leading to inefficiency. To solve these problems, an improved deep forest (IgcForest) classification algorithm is proposed in this paper. IgcForest first preserves the class distribution vectors of the main features by a pooling strategy to realise the feature reduction and reuse. Next, an adaptive weighting strategy is proposed to calculate the weight of each forest in the cascade structure. Experiments are carried out on multiple UCI datasets to verify the effectiveness of IgcForest and the results demonstrate that IgcForest achieves better results in different evaluation indexes than other algorithms.
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