An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition
This work addresses a domain-specific issue for power industry customer service systems, offering an incremental improvement in fault diagnosis efficiency.
The paper tackles the problem of inefficient fault type recognition in power ICT custom services by proposing BR-GBDT, an ensemble learning approach for multi-label text classification, which outperforms existing methods like BR+LR and ML-KNN in experiments.
With the rapid development of ICT Custom Services (ICT CS) in power industries, the deployed power ICT CS systems mainly rely on the experience of customer service staff for fault type recognition, questioning, and answering, which makes it difficult and inefficient to precisely resolve the problems issued by users. To resolve this problem, in this paper, firstly, a multi-label fault text classification ensemble approach called BR-GBDT is proposed by combining Binary Relevance and Gradient Boosting Decision Tree for assisted fault type diagnosis and improving the accuracy of fault type recognition. Second, for the problem that there is lack of the training set for power ICT multi-label text classification, an automatic approach is presented to construct the training set from the historical fault text data stored in power ICT CS systems. The extensive experiments were made based on the power ICT CS training set and some general-purpose benchmark training datasets. The experiment results show that our approach outperforms the well known ensemble learning based approaches BR+LR and ML-KNN for fault text classification, efficiently handling the multi-label classification of ICT custom service text data for fault type recognition.