Article citationsMore>>

Chen, Y.Z., Chen, W., Chandra Pal, S., Saha, A., Chowdhuri, I., Adeli, B., Janizadeh, S., Dineva, A.A., Wang, X.J. and Mosavi, A. (2022) Evaluation Efficiency of Hybrid Deep Learning Algorithms with Neural Network Decision Tree and Boosting Methods for Predicting Groundwater Potential. Geocarto International, 37, 5564-5584. https://doi.org/10.1080/10106049.2021.1920635

has been cited by the following article:

• TITLE: Heavy-Head Sampling Strategy of Graph Convolutional Neural Networks for q-Consistent Summary-Explanations with Application to Credit Evaluation Systems

  AUTHORS: Xinrui Dou

  KEYWORDS: Summary-Explanation, q-Consistent, Branch-and-Bound, Heavy-Head Sampling Strategy

  JOURNAL NAME: Open Access Library Journal, Vol.10 No.9, September 15, 2023

  ABSTRACT: Machine learning systems have found extensive applications as auxiliary tools in domains that necessitate critical decision-making, such as healthcare and criminal justice. The interpretability of these systems’ decisions is of paramount importance to instill trust among users. Recently, there have been developments in globally-consistent rule-based summary-explanation and its max-support (MSGC) problem, enabling the provision of explanations for specific decisions along with pertinent dataset statistics. Nonetheless, globally-consistent summary-explanations with limited complexity tend to have small supports, if any. In this study, we propose a more lenient variant of the summary-explanation, namely the q-consistent summary-explanation, which strives to achieve greater support at the expense of slightly reduced consistency. However, the challenge lies in the fact that the max-support problem of the q-consistent summary-explanation (MSqC) is significantly more intricate than the original MSGC problem, leading to extended solution times using standard branch-and-bound (B & B) solvers. We improve the B & B solving process by replacing time-consuming heuristics with machine learning (ML) models and apply a heavy-head sampling strategy for imitation learning of MSqC problems by exploiting the heavy-head maximum depth distribution of B & B solution trees. Experimental results show that using the heavy-head sampling strategies, the final evaluation results of trained strategies on MSqC problems are significantly improved compared to previous studies using uniform sampling strategies.