A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy
Abstract
Text classification, the task of metadata to documents, requires significant time and effort when performed by humans. Moreover, with online-generated content explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Currently, lots of state-or-art text mining methods have been applied to classification process, many of them based on the key word extraction. However, when using these key words as features in classification task, it is common that feature dimension is huge. In addition, how to select key words from tons of documents as features in classification task is also a challenge. Especially when using tradition machine learning algorithm in the large data set, the computation cost would be high. In addition, almost 80% of real data is unstructured and non-labeled. The advanced supervised feature selection methods cannot be used directly in selecting entities from massive of data. Usually, extracting features from unlabeled data for classification tasks, statistical strategies have been utilized to discover key features. However, we propose a nova method to extract important features effectively before feeding them into the classification assignment. There is another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to the documents. This problem makes text classification more complicated when compared with single label classification. Considering above issues, we develop a framework for extracting and eliminating data dimensionality, solving the multi-label problem on labeled and unlabeled data set. To reduce data dimension, we provide 1) a hybrid feature selection method that extracts meaningful features according to the importance of each feature. 2) we apply the Word2Vec to represent each document with a lower feature dimension when doing the document categorization for the big data set. 3) An unsupervised approach to extract features from real online generated data for text classification and prediction. On the other hand, to solve the multi-label classification task, we design a new Multi-Instance Multi-Label (MIML) algorithm in the proposed framework.