臺灣博碩士論文加值系統

本篇論文提出一個以模糊亂度為基礎，並具有特徵選取能力之有效率的分類器。在這個分類器中，我們利用模糊亂度來計算各類樣本在樣本空間中的分佈情形，並根據所獲得的樣本分佈資訊，將樣本空間切割成非重疊的判斷區域(non-overlapped decision regions)。在判斷區域形成後，即可用來分辨未知的樣本。由於此分類器的判斷區域並不互相重疊，故此分類器的架構複雜度低、運算量少，所消耗的時間亦很短。因為這些判斷區域是由模糊子空間(fuzzy subspaces)所組成的，即使判斷區域不互相重疊，此分類器仍可經由模糊子空間所產生的平滑分界曲線(smooth decision boundaries)，得到不錯的分類結果。此外，我們亦提出一個以模糊亂度為基礎的特徵選取法則，此特徵選取法則不僅降低了問題的複雜度，同時亦提昇了分類器的辨識率，這是因為經過特徵選取後，某些多餘、不重要或有雜訊干擾的特徵，即會被刪除不再使用。因此，具有特徵選取能力的分類器，可以縮短運算時間，並提高分類的效能。最後，經由花類辨識(Iris classification)和胸腔疾病診斷(Breast cancer diagnosis)的實驗結果顯示，此分類器可以有效的應用在樣本分類的問題。

This thesis presents an efficient fuzzy classifier with the ability of feature selection based on fuzzy entropy measure. The fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With such information, we can apply it to partition the pattern space into non-overlapped decision regions for pattern classification. Since the decision regions do not overlap, the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely fast. Although the decision regions are partitioned as non-overlapped subspaces, we can also achieve good performance by the produced smooth boundaries since the decision regions are fuzzy subspaces. In addition, we also investigate a fuzzy entropy-based method to select the relevant features. The feature selection procedure not only reduces the dimension of a problem but also discards the noise-corrupted, redundant or unimportant features. As a result, the time consuming of the classifier is reduced whereas the classification performance is increased. Finally, we apply the proposed classifier on the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification applications.