分類問題為常見於日常生活且重要的研究議題，例如醫生或許希望在醫學檢測前知道病人是否有癌症，使用者在下載電子郵件前知道是正常郵件或垃圾郵件。資料探勘中決策樹(decision tree)、區別分析(linear discriminant analysis)、倒傳遞類神經網路(back-propagation network)、支援向量機(support vector machine)皆已成功應用在各種領域分類問題中。然而資料探勘演算法在面對不同特性問題時需要不同的參數設定以建立出好的模型，於是試誤法常被用以解決參數決定問題。但其除費時外更可能因參數設定不佳而得到不好的結果，另一方面資料集中通常包含許多屬性但並非所有屬性皆對於建立分類模型有益。
通用啟發式演算法(meta-heuristic algorithm)已被成功的應用在最佳化問題之中，本論文利用啟發式演算法中模擬退火演算法(simulated-annealing)、粒子群最佳化演算法(particle swarm optimization algorithm)與分散式搜尋演算法(scatter search algorithm)找出在不同問題下對資料探勘演算法合適的參數組合與屬性集合以建立更佳的模型。
另外不同資料探勘演算法在面對不同特性問題時有其各自的優點與缺點，若能將這些演算法預測整合可得到更佳結果，此種結合多演算法預測稱之為合併預測(combining predictors)。因此本論文利用合併多個經由良好參數設定與屬性篩選之分類器由整併多個分類器之預測更進一步的強化正確率。
本論文為衡量所提出方法之效果利用UCI公開資料庫中多個資料集做為測試基準與多個改良資料探勘文獻相比較。實驗結果顯示本論文所提出之方法能比其他的研究提供更佳的分類正確率，顯示本論文提出之方法能在不同特性的問題下皆能提供良好的分類正確率。

Classification problem is an important research issue of real world. For example, the doctor may want to detect the patient has cancer or not before medical test. The user wants to know an email is legitimate or spam before download the mail. The decision tree (DT), linear discriminant analysis (LDA), back-propagation network (BPN), and support vector machine (SVM) of data mining algorithms are popular and can be applied to various areas successfully. However, different problems typically require different parameter settings. Rule of thumb or trial-and-error methods are generally utilized to determine parameter settings. However, these methods may result in poor parameter settings and unsatisfactory results. On the other hand, although a dataset contain many features, not all features are beneficial for classification in data mining algorithms. Due to the population-based meta-heuristics have been applied to various optimization problems successful, three meta-heuristics, including simulated-annealing (SA), particle swarm optimization (PSO), and scatter search (SS), are proposed to obtain appropriate parameters and select the beneficial subsets of features for data mining algorithms in this study.
Moreover, the different data mining algorithms have their respective advantages and disadvantages, and suitability is influenced by the characteristics of the problem. If the algorithms can function together in the so-called combining predictors, it is expected that better results can be obtained. Therefore, this study adapts combining predictors to further enhance the classification accuracy rate.
In order to evaluate the performance of the proposed approach, datasets in UCI (University of California, Irvine) data mining database were applied as the test problem set. The experimental results were compared to several well-known, published approaches. The comparative study shows that the proposed approach improved the classification accuracy rate in most datasets. Thus, the proposed approach can be useful to different problems and provided good classification accuracy rate.

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