## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Definition** \\ \hline \(m_{i}\) & The sum of the prices of all the raw materials needed in the process of appliance manufacturing (\(i\)) \\ \(p_{i}\) & The price at which a home appliance producer sells home appliance appliance \(i\) to a seller \\ \(s_{i}\) & The price at which a seller sells appliance \(i\) to a consumer \\ \(c_{i}\) & The price at which a consumer sells home appliance \(i\) to a second-hand dealer or \\ \(rs_{i}\) & The price at which a second-hand dealer bring home appliance \(i\) back into the market \\ \(b_{i}^{m}\) & The selling price of the m-th-level business recycler for household appliance \(i\) to the a next-level recycler \\ \(p_{i}^{n}\) & The selling price of the n-th-level personal recycler for household appliance \(i\) to a next-level recycler \\ \(d_{i}\) & The price of materials sold by a disintegrator to reproducucers or home appliance \\  & producers after dismantling home appliance \(i\) \\ \(rp_{i}\) & The price of raw materials that reproducucers sell to home appliance producers after \\  & reprocessing the recycled materials from home appliance \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prices of materials at various stages of the manufacturing and recycling process. mobile phones in Turkey, but they did not apply the estimated results to recycling appraisal. No effective evaluation method for waste household appliance recycling has been proposed in the literature because the evaluation of household appliance recycling has unique characteristics and should be focused on waste raw materials as the main evaluation factor and combined with market game modeling to price the entire recycling chain. The most important entry point is the valuation of waste household appliances in the hands of consumers flowing into the recycling market, which is the most important part of the entire evaluation process in the recycling chain [19]. Machine learning methods have not yet been introduced for the evaluation of recycled waste household appliances. The traditional method of evaluating waste household appliances is usually based on manual experience and rules. As a comparison, machine learning methods can adaptively learn and process new data and continuously improve prediction accuracy. Moreover, machine learning can process a large amount of data, optimize algorithms, and improve the accuracy as and speed of waste household appliance valuation while enabling intelligent decision making in support of the recycling industry. In this paper, we focus on value assessment for waste household appliances (i.e., the \(c_{i}\) data in Figure 1) and propose a machine-learning-based evaluation model for recycling waste household appliances. The three main contributions of this paper are as follows:  
* A subtractive clustering method and adaptive network fuzzy inference system (SCM\(-\)ANFIS)-based evaluation algorithm is used to evaluate the waste TELEVISION dataset, achieving better results in terms of mean absolute percentage error (MAPE) than long short-term memory (LSTM), BP neural network, random forest (RF), and Takagi-Sugeno fuzzy neural network (T\(-\)S FNN);
* A combination evaluation model based on maximum ratio combination (CEM\(-\)MRC) is constructed based on the machine learning methods mentioned above and compared with different combination methods to predict results. The results show that the maximum ratio combination method achieves the best performance among tested combination methods, with a lower MAPE than the suboptimal BP method, reducing it by 0.1%;
* An enhanced evaluation model based on classification selection (EEM\(-\)CS) is designed by combining the prediction models of CEM\(-\)MRC and SCM\(-\)ANFIS, which automatically selects the evaluation results between them. This method reduces the MAPE by 0.73% compared to the optimal SCM\(-\)ANFIS method and by 1.42% compared to the suboptimal CEM\(-\)MRC method on the training set. The remainder of this paper is organized as follows.