# An Ensemble Learning Based Approach to Multi-label Power Text Classification for Fault-type Recognition

arXiv: https://arxiv.org/abs/2204.06179 · HTML: https://mlsanity.com/paper/2204.06179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [An Analysis of Hierarchical Text Classification Using Word Embeddings](https://mlsanity.com/paper/1809.01771/) (2018)
- [Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification](https://mlsanity.com/paper/1911.01658/) (2019)

## Abstract (arXiv)

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.