Abstract

Gemstones, such as diamonds, are used in various applications, from jewelry to technology, where they have recently been considered as semiconductor materials. However, the value of diamonds is difficult to measure due to their price being influenced by characteristics such as cut, color, clarity, and carat weight, making the estimation of diamond value a complex and sometimes subjective task. Currently, regression models are being developed to estimate the value of these precious stones. To support the estimation of diamond value and improve the training time of predictive models, this research proposes the multiclass classification of diamond values using standard ELM, regularized ELM, and weighted ELM. The classification was based on 4 value categories with respect to their prices: (a) less than US$500, (b) between US$500 and US$1000, (c) between US$1000 and US$1500, and (d) over US$1500. The results obtained are presented based on accuracy and model training time. Of the evaluated models, the regularized ELM presented the best results, with an accuracy of 0.8375 and a runtime of 109 seconds. The results demonstrate that ELMs can efficiently classify diamond prices, and the models are robust, showing the price trend, and the main classification errors of the models are generated in classes with prices very similar between diamonds.