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Analyzing foreign exchange rates by rough set theory and directed acyclic graph support vector machines

機(jī)譯：用粗糙集理論和有向無環(huán)圖支持向量機(jī)分析匯率

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Rough set theory (RST) and directed acyclic graph support vector machines (DAGSVM) are two emerging techniques in dealing with classification problems. The RST approach is able to select important features and generate rules from data. The SVM technique is powerful in solving classification problems with high generalization ability by applying the structure risk minimization principle. However, one particular model cannot capture all data patterns easily. This investigation presents a hybrid RST and DAGSVM model (RSTDAGSVM) to exploit the unique strengths of both RST and SVM in analyzing the movements of exchange rates. In the proposed hybrid model, the RST approach is used to extract the rules of exchange rate changes; and the DAGSVM technique is employed to deal with situations that cannot be included in the RST model. In addition, an immune algorithm and tabu search (IA/TS) method is applied to select parameters of SVM models. Experimental results reveal that the developed model achieves more accurate prediction results than either the RST model or the DAGSVM model on its own. Thus, the presented RSTDAGSVM model is a promising alternative for analyzing exchange rates.
機(jī)譯：粗糙集理論（RST）和有向無環(huán)圖支持向量機(jī)（DAGSVM）是處理分類問題的兩種新興技術(shù)。 RST方法能夠選擇重要功能并根據(jù)數(shù)據(jù)生成規(guī)則。通過應(yīng)用結(jié)構(gòu)風(fēng)險(xiǎn)最小化原則，SVM技術(shù)在解決具有高泛化能力的分類問題方面功能強(qiáng)大。但是，一個(gè)特定的模型無法輕松捕獲所有數(shù)據(jù)模式。這項(xiàng)研究提出了一種RST和DAGSVM混合模型（RSTDAGSVM），以利用RST和SVM的獨(dú)特優(yōu)勢來分析匯率變動(dòng)。在提出的混合模型中，RST方法用于提取匯率變動(dòng)規(guī)則。 DAGSVM技術(shù)用于處理無法包含在RST模型中的情況。另外，采用免疫算法和禁忌搜索（IA / TS）方法選擇支持向量機(jī)模型的參數(shù)。實(shí)驗(yàn)結(jié)果表明，與單獨(dú)使用RST模型或DAGSVM模型相比，所開發(fā)的模型可獲得更準(zhǔn)確的預(yù)測結(jié)果。因此，所提出的RSTDAGSVM模型是用于分析匯率的有希望的替代方法。

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