TY - GEN
T1 - Research on machine learning method-based combination forecasting model and its application
AU - Sun, Zhenlong
AU - Zhu, Conghui
AU - Xu, Bing
AU - Li, Sheng
PY - 2011
Y1 - 2011
N2 - A novel combination forecasting model is presented in this paper, which combines single ones based on machine learning. The model has been applied to the prediction of five cities' election in Taiwan with combining the exposure rate and the approval rate, which obtains good results. The exposure rate is the frequency of a candidate's appearances in the news and approval rate is the proportion of the positive information of a candidate. And the polarity of a review is predicted by sentiment classification based on machine learning techniques. A novel method of feature extraction is used in sentiment classification, which makes the classifier effectively assign the review a type of polarity. Meanwhile, this paper proposes a method of feature clustering and extending based on the synonym dictionary, which obviously reduces the dimension of feature vector and improve the F-score of sentiment classification.
AB - A novel combination forecasting model is presented in this paper, which combines single ones based on machine learning. The model has been applied to the prediction of five cities' election in Taiwan with combining the exposure rate and the approval rate, which obtains good results. The exposure rate is the frequency of a candidate's appearances in the news and approval rate is the proportion of the positive information of a candidate. And the polarity of a review is predicted by sentiment classification based on machine learning techniques. A novel method of feature extraction is used in sentiment classification, which makes the classifier effectively assign the review a type of polarity. Meanwhile, this paper proposes a method of feature clustering and extending based on the synonym dictionary, which obviously reduces the dimension of feature vector and improve the F-score of sentiment classification.
KW - combination forecasting model
KW - feature clustering
KW - feature extraction
KW - sentiment classification
UR - https://www.scopus.com/pages/publications/80053392608
U2 - 10.1109/FSKD.2011.6019650
DO - 10.1109/FSKD.2011.6019650
M3 - 会议稿件
AN - SCOPUS:80053392608
SN - 9781612841816
T3 - Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
SP - 1226
EP - 1231
BT - Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
T2 - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11
Y2 - 26 July 2011 through 28 July 2011
ER -