Explainable Deep Belief Network based Auto encoder using novel Extended Garson Algorithm
This provides an incremental improvement in explainable AI for researchers and practitioners needing feature importance in deep learning models.
The paper tackles the problem of interpreting deep neural networks by extending the Garson Algorithm to explain Deep Belief Network-based Auto-encoders, resulting in better-quality feature identification for 4 out of 9 datasets compared to Wald chi-square.
The most difficult task in machine learning is to interpret trained shallow neural networks. Deep neural networks (DNNs) provide impressive results on a larger number of tasks, but it is generally still unclear how decisions are made by such a trained deep neural network. Providing feature importance is the most important and popular interpretation technique used in shallow and deep neural networks. In this paper, we develop an algorithm extending the idea of Garson Algorithm to explain Deep Belief Network based Auto-encoder (DBNA). It is used to determine the contribution of each input feature in the DBN. It can be used for any kind of neural network with many hidden layers. The effectiveness of this method is tested on both classification and regression datasets taken from literature. Important features identified by this method are compared against those obtained by Wald chi square (\c{hi}2). For 2 out of 4 classification datasets and 2 out of 5 regression datasets, our proposed methodology resulted in the identification of better-quality features leading to statistically more significant results vis-à-vis Wald \c{hi}2.