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

TL;DR

This paper introduces an ensemble learning method called BR-GBDT for multi-label fault text classification in power ICT systems, improving fault type recognition accuracy by combining Binary Relevance and Gradient Boosting Decision Trees.

Contribution

It proposes a novel ensemble approach for multi-label fault text classification and an automatic training set construction method from historical data, addressing data scarcity issues.

Findings

BR-GBDT outperforms BR+LR and ML-KNN in accuracy

Effective handling of multi-label fault classification

Improved fault diagnosis in power ICT systems

Abstract

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.