Feasibility analysis of machine learning for performance-related attributional statements

Berkin, Anil; Aerts, Walter; Caneghem, Tom Van

doi:10.1016/j.accinf.2022.100597

Cited by 9 publications

(1 citation statement)

References 93 publications

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“…In order to verify the efficiency of the proposed method, traditional machine learning models, such as SVMs [26], KNN [27], and Naive Bayes [28], and deep learning models and their variants, including CNNs [29], LSTM [30], BERT-CNN [31], LSTM-CNN [32], and LDA-Ngram-BERT-LSTM, were used for comparison.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Building Materials Classification Model Based on Text Data Enhancement and Semantic Feature Extraction

Yan,

Jiao,

Peng

2024

Buildings

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In order to accurately extract and match carbon emission factors from the Chinese textual building materials list and construct a precise carbon emission factor database, it is crucial to accurately classify the textual building materials. In this study, a novel classification model based on text data enhancement and semantic feature extraction is proposed and applied for building materials classification. Firstly, the explanatory information on the building materials is collected and normalized to construct the original dataset. Then, the Latent Dirichlet Allocation and statistical-language-model-based hybrid ensemble data enhancement methods are explained in detail, and the semantic features closely related to the carbon emission factor are extracted by constructed composite convolutional networks and the transformed word vectors. Finally, the ensemble classification model is designed, constructed, and applied to match the carbon emission factor from the textual building materials. The experimental results show that the proposed model improves the F1Macro score by 4–12% compared to traditional machine learning and deep learning models.

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