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An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery

机译:一种主题词典的激活方法,用于扩展训练数据以发现趋势规则

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This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments.
机译:本文改进了一种预测公司或产品等评估对象在不久的将来是否具有吸引力的方法。吸引力通过趋势规则进行评估。趋势规则表示评估对象,关键字和与评估对象相关的数值变化之间的关系。它们是从文本顺序数据和数字顺序数据中归纳获取的。该方法通过激活主题字典将评估对象分配给文本顺序数据。词典中描述了代表数字变化的关键字。它可以扩展训练数据的数量。可以预期,扩展将导致获得更多有效的趋势规则。本文将这种方法应用于根据新闻标题和股票价格序列预测有吸引力的股票品牌的任务。通过数值实验表明,该方法可以提高评价对象的检测性能。

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  • 来源
    《Applied computational intelligence and soft computing》 |2014年第2014期|871412.1-871412.11|共11页
  • 作者单位

    IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, Japan;

    IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, Japan;

    IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, Japan;

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