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A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics
Yamamoto, Yusei,Mikawa, Kenta,Goto, Masayuki Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.2
With rapid development on information society, automatic document classification by machine learning has become even more important. In document classification, it is assumed that a new input data can be classified into any of the categories observed in the training data. Therefore, if a new input data belongs to an unobserved category which does not exist in the training data, then such data cannot be classified exactly. To solve the above problem, Arakawa et al. proposed the method which models the generative probabilities of documents with a mixture of Polya distributions and estimates the optimum category within all observed and unobserved categories where it is assumed that documents in each category are generated from each single Polya distribution. However, the statistical characteristics of document categories are generally more complicated and there are various underlying latent topics in a category. Because a single Polya distribution models each category in the conventional approach, this method cannot represent the variation of word frequency depending on plural unobserved latent topics. This paper proposes a new model which assumes a mixture of Polya distributions for the generative probabilities of documents in a category to represent plural latent topics. To verify the effectiveness of the proposed method, we conduct the simulation experiments of document classification by using a set of English newspaper articles.
A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics
Yusei Yamamoto,Kenta Mikawa,Masayuki Goto 대한산업공학회 2017 Industrial Engineeering & Management Systems Vol.16 No.2
With rapid development on information society, automatic document classification by machine learning has become even more important. In document classification, it is assumed that a new input data can be classified into any of the categories observed in the training data. Therefore, if a new input data belongs to an unobserved category which does not exist in the training data, then such data cannot be classified exactly. To solve the above problem, Arakawa et al. proposed the method which models the generative probabilities of documents with a mixture of Polya distributions and estimates the optimum category within all observed and unobserved categories where it is assumed that documents in each category are generated from each single Polya distribution. However, the statistical characteristics of document categories are generally more complicated and there are various underlying latent topics in a category. Because a single Polya distribution models each category in the conventional approach, this method cannot represent the variation of word frequency depending on plural unobserved latent topics. This paper proposes a new model which assumes a mixture of Polya distributions for the generative probabilities of documents in a category to represent plural latent topics. To verify the effectiveness of the proposed method, we conduct the simulation experiments of document classification by using a set of English newspaper articles.
Fuzzy Logic and Causal Analysis in Biomedical Sensing System
Hiroshi Nakajima,Naoki Tsuchiya,Kenta Yamamoto,Yutaka Hata 한국지능시스템학회 2010 한국지능시스템학회 학술발표 논문집 Vol.20 No.2
This article proposes the combination of fuzzy logic and causal analysis regarding biomedical sensing. As one of the applications, a heart rate monitoring system is introduced. It monitors heart rate of human body during sleep or lying on the bed. The system consists of an air pressure sensor and data analysis computation part. Fuzzy logic is employed as a role of filtering heart beat signals from an air pressure sensor data which superimposes vibration signals of heart beat, body movement, and respiration. Causal analysis plays a role of making transparent of calculation flow in heart rate from the extracted heat beat signals. The experiments were conducted to prove the performance of the proposed method. Experimental results were very high correlation coefficient of around 0.85.