A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction
Toly Chen, Yu-Cheng Wang
Abstract
Methods based on artificial neural network (ANN) or deep neural network (DNN) applications have been proposed to predict job cycle time effectively. However, the predicting mechanism of ANNs (or DNNs) is often difficult to understand and communicate. This problem has hindered their acceptability (and practicability). Furthermore, existing explainable artificial intelligence (XAI) techniques use simpler decision rules to approximate the prediction process and/or outcomes of ANNs (or DNNs). However, these decision rules may not conform to domain knowledge, which limits their usefulness. This study proposes a modified random forest incremental interpretation (RFII) approach to address this issue. In the proposed methodology, the input–output relationship of an ANN (or DNN) is fitted by decision rules using a random forest approach. Then, domain knowledge is extracted, and decision rules are checked against this knowledge. Decision rules with poor conformance to domain knowledge are excluded. Subsequently, the remaining decision rules are aggregated in a way that is convenient for user interpretation. The modified RFII method has been applied to a real case in the literature to demonstrate its applicability.