Enhanced Evaluation Model Based on Classification Selection Applied to Value Evaluation of Waste Household Appliances

Abstract

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms.