Hierarchical Interpretable Construction Algorithm for Data Analysis

In low-computation modeling tasks, the Incremental Constructive Algorithm (ICA) shows good performance. However, it relying on horizontal network expansion for model building limits its mapping ability. Thus, it struggles to capture deep feature relations in complex data and can not meet accuracy ne...

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Bibliographic Details
Published in:	2025 30th International Conference on Automation and Computing (ICAC) pp. 1 - 5
Main Authors:	Dai, Wei, Duan, MingZi, Nan, Jing
Format:	Conference Proceeding
Language:	English
Published:	IEEE 27.08.2025
Subjects:	Accuracy Computational efficiency Computational modeling Convergence Data analysis Geometry geometry control strategy Heuristic algorithms hierarchical deep structure Interpretable constructive algorithm Network architecture Prediction algorithms Stacking
Online Access:	Get full text
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Summary:	In low-computation modeling tasks, the Incremental Constructive Algorithm (ICA) shows good performance. However, it relying on horizontal network expansion for model building limits its mapping ability. Thus, it struggles to capture deep feature relations in complex data and can not meet accuracy needs in complex predictions. This paper proposes the Hierarchical Incremental Construction Algorithm (HICA) by combining ICA interpretability control strategy and DNNs deep stacking structure. HICA has hierarchical propagation. It uses the previous layer residual as the next layer predicted value and the previous hidden layer output matrix as input. It can dynamically add hidden layers and iteratively replace actual outputs with expected ones for better generalization. Nodes are selected by an interpretability-based geometric control strategy. Experiments prove HICA accuracy advantage and lower computational cost.
DOI:	10.1109/ICAC65379.2025.11196560