  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.
