根據模糊分群(Fuzzy C-Means[FCM])理論、變精準粗集合(Variable Precision Rough Set[VPRS])理論和SVP-index函數,本研究提出一種新的混合監督式分類方法,稱為FVS方法,用於解決標記資料集的分類問題。 本研究利用UCI的資料集,針對FVS方法的有效性進行研究,與類神經網路(Artificial Neural Networks [ANN])、決策樹(Decision Tree[DT])以及Huang-index分類方法的結果作比較,結果證明,FVS方法不僅可以成功地挑出不精確及不確定的資料,並且獲得較可靠的決策規則。
A classification method, designated as the FVS method, comprises a Fuzzy C-Means (FCM) method, Variable Precision Rough Set (VPRS) theory and SVP-index function for classifying labeled datasets in this study. The validity of the proposed approach is confirmed by comparing the classification results obtained from the FVS method for UCI datasets with those obtained from the supervised neural networks, Decision Tree and Huang-index classification method. This study provides a better solution for the generation of reliable decision rules for labeled classification problems.