基礎分類方法為單一模型，常見方法有簡易貝氏分類方法和支持向量機，在對測試樣本進行類別預測時，依據原本測試樣本屬性資料進行分類預測，最終得到最有可能的類別預測值，對分類預測結果容易闡釋，然而分類結果穩定性並不佳；而後學者提出集成式分類方法，常見有掛袋法與推進法，產生多個預測模型，最終由多數決投票決定結果，此方法分類結果較穩定，但無法對分類預測結果闡釋。混合式分類方法是基礎分類方法的延伸，其核心概念是藉由產生多個模型，但每次預測挑只選其中一個來做預測，可改善擔心單一個分類模型結果不穩定問題，以及多組模型做預測而無法對結果具有解釋力的情況。因此本研究考量各分類方法特性，首先依據某分類方法做特徵選取後，產生多個模型，並設計一種機制來選擇單一模型來進行預測，既可以對結果具有闡釋性，也能改善擔心單一個分類模型做決策結果不穩定問題。實證研究中本研究使用UCI中15個資料集進行實證，可以發現本研究的方法相較於基礎分類方法，半數以上資料集上的正確率有所改善；本而研究的方法相較於經過特徵選取之基礎分類方法，半數以上的資料集上有所改善，相對之下改善幅度比較大，但是有些資料檔集分類正確率比較低；而第一階段與第二階段分類方法相同與不相同時，運算出來的正確率差距並不大。 本研究一些數據集的性能無法提高，因為大多數測試資料都是根據從全特徵集導出的模型進行分類的。 這意味著混合分類方法的正確率的改進在很大程度上取決於數據集的特徵。

Basic classification algorithms, such as naïve Bayesian classifier and support vector machine, induce a single model from training data. Every prediction made by the model is easy to interpret, while its accuracy is generally not competitive. Ensemble algorithms induce several models from training to make group decisions for class prediction. An ensemble algorithm can thus achieve a relatively high accuracy, while it is difficult to interpret a prediction made by multiple models. Hybrid classification algorithms induce several models from training data, and only one of the models will be chosen to classify a testing instance. This kind of predictions is still easy to interpret, and the prediction accuracy is likely to be improved. This study proposes a scheme based on feature selection to build hybrid classification algorithms. The attribute set corresponding to a data set is divided into disjoint primary and secondary attribute sets by a wrapper that employs a basic classification algorithm to perform feature selection. Then the full, primary, and secondary attribute sets are used to train a basic algorithm that can be the same as or different from the one for feature selection. Four hybrid algorithms composed of logistic regression and support vector machine are tested on 15 data set downloaded from the UCI data repository. The experimental results show that the hybrid algorithms outperform the corresponding ones in approximately half of the data set. The basic algorithms for feature selection and model induction seem to have little impact on their performance. The performance of some data sets cannot be improved because most of the testing instances are classified by the model induced from the full attribute set. This implied that the performance improvement of hybrid algorithms highly depends on the characteristics of data sets.

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