• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.270-277
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• 2002

Text filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. The aim of this paper is to improve the accuracy of text filtering systems by using machine learning techniques. We apply AdaBoost algorithms to the filtering task. An AdaBoost algorithm generates and combines a series of simple hypotheses. Each of the hypotheses decides the relevance of a document to a topic on the basis of whether or not the document includes a certain word. We begin with an existing AdaBoost algorithm which uses weak hypotheses with their output of 1 or -1. Then we extend the algorithm to use weak hypotheses with real-valued outputs which was proposed recently to improve error reduction rates and final filtering performance. Next, we attempt to achieve further improvement in the AdaBoost's performance by first setting weights randomly according to the continuous Poisson distribution, executing AdaBoost, repeating these steps several times, and then combining all the hypotheses learned. This has the effect of mitigating the ovefitting problem which may occur when learning from a small number of data. Experiments have been performed on the real document collections used in TREC-8, a well-established text retrieval contest. This dataset includes Financial Times articles from 1992 to 1994. The experimental results show that AdaBoost with real-valued hypotheses outperforms AdaBoost with binary-valued hypotheses, and that AdaBoost iterated with random weights further improves filtering accuracy. Comparison results of all the participants of the TREC-8 filtering task are also provided.

• Journal of Preventive Medicine and Public Health
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• v.49 no.5
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• pp.329-341
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• 2016

Objectives: The goal of this study was to investigate the short-term effect of ambient air pollution on emergency department (ED) visits in Seoul for asthma according to patients' prior history of allergic diseases. Methods: Data on ED visits from 2005 to 2009 were obtained from the Health Insurance Review and Assessment Service. To evaluate the risk of ED visits for asthma related to ambient air pollutants (carbon monoxide [CO], nitrogen dioxide [$NO_2$], ozone [$O_3$], sulfur dioxide [$SO_2$], and particulate matter with an aerodynamic diameter <$10{\mu}m$ [$PM_{10}$]), a generalized additive model with a Poisson distribution was used; a single-lag model and a cumulative-effect model (average concentration over the previous 1-7 days) were also explored. The percent increase and 95% confidence interval (CI) were calculated for each interquartile range (IQR) increment in the concentration of each air pollutant. Subgroup analyses were done by age, gender, the presence of allergic disease, and season. Results: A total of 33 751 asthma attack cases were observed during the study period. The strongest association was a 9.6% increase (95% CI, 6.9% to 12.3%) in the risk of ED visits for asthma per IQR increase in $O_3$ concentration. IQR changes in $NO_2$ and $PM_{10}$ concentrations were also significantly associated with ED visits in the cumulative lag 7 model. Among patients with a prior history of allergic rhinitis or atopic dermatitis, the risk of ED visits for asthma per IQR increase in $PM_{10}$ concentration was higher (3.9%; 95% CI, 1.2% to 6.7%) than in patients with no such history. Conclusions: Ambient air pollutants were positively associated with ED visits for asthma, especially among subjects with a prior history of allergic rhinitis or atopic dermatitis.