Improved Stacking Model Fusion Based on Weak Classifier and Word2vec
Jun Liu, Wenqian Shang, Weiguo Lin · 2018
Stacking model Fusion is a combination classification method for natural language processing and text categorization. Compared to a single weak classifier, model Fusion has the advantage of combining the classification strengths of multiple classifiers, so the combination classifier is often more accurate than a single classifier, and the research of this field has been developed rapidly in recent years, and the combination classifier has been applied in various natural language processing tasks. But only by using the prediction results of the first layer weak classifier to train the second layer classifier, it has a strong limitation, only considers the training of the classification result and ignores the semantic information. We think that the method of training the weak classifier by TFIDF to the document, the expression of the document is not enough, only the information about the frequency of the document and the document is lack of the semantic information of the word2vector. In this paper, a new combination classification method is proposed, which combines the various weak classifiers trained by TFIDF and Word2vector to express the documents in many aspects, and the feature expression can fully utilize the information provided by the document. It has better classification effect than individual word2vector expression and classification and simple weak classifier combination classification.