关键词: 深度森林, 特征子集, 表征学习能力, 环状扫描

Abstract:

Deep Forest（DF） model has more advantages than neural network algorithm in dealing with large-scale data, because DF has few super parameters, no too many requirements for parameters setting, convenient training and high robustness. However, in classical deep forest, the multi grained scanning ignores the hidden information carried by the edge data, and cannot fully obtain each feature subset, which affects the following cascading part. Moreover, the new features obtained by the cascading part are limited each time, which affects the representation learning ability of DF. To solve the above problems, Circular Strong Deep Forest（CSDF） is proposed in this paper. Through the circular scanning process, more sufficient feature subsets are obtained in CSDF. In addition, the strong cascaded forest in CSDF improves the representation learning ability of the model through feature selection. After testing on different typical data sets, the results show that CSDF is more superior in performance, and especially obvious in high-dimensional data.

Key words: deep forest, feature subset, representation learning ability, circular scanning