這篇論文我們提出了建構在Kohonen的SOM [7]上處理混合特徵型(包括符號型(symbolic type)與模糊型(fuzzy type))資料的自我組織映射學習網路。文中的混合特徵資料(mixed feature data)的距離測量,主要是根據Gowda & Diday [3,4]、El-Sonbaty & Ismail [2]、Yang , Hwang & Chen [9]及Yang & Ko [10]的距離定義方式,將其建構在自我組織特徵映射學習網路(SOM)上,即所謂的混合特徵資料的自我組織特徵映射(MFD-SOM),並提出修正MFD-SOM的方法,另稱為混合特徵資料的修正式自我組織特徵映射(MFD-M-SOM)。另一方面也採用Wu & Yang [8]的模糊軟性學習(fuzzy-soft learning)方法,同樣的建構在SOM上,稱為混合特徵資料的模糊軟性自我組織特徵映射(MFD-FS-SOM)。在類神經元的學習方面,我們提出新的轉換方法與學習方式,使類神經元做適當的調整,再藉由混合特徵映射圖來呈現資料的主要特徵。最後,我們做了電腦模擬比較並應用到實際資料上,發現MFD-M-SOM演算法優於MFD-FS-SOM演算法,MFD-FS-SOM演算法優於MFD-SOM演算法。
In this paper we propose a Kohonen’s self-organizing map (SOM) for mixed feature (symbolic type and fuzzy type) data. The distance measures proposed in Gowda & Diday[3,4], El-Sonbaty & Ismail[2], Yang, Hwang & Chen[9] and Yang & Ko[10] are used in this paper. Based on these distance measuces we proposed the mixed feature data SOM (MFD-SOM). We then propose a modified type of MFD-SOM method, called the modified self-organizing map for mixed feature data (MFD-M-SOM). On the other hand, we use fuzzy-soft learning method of Wu & Yang[8], and then create the fuzzy-soft self-organizing feature map for mixed feature data (MFD-FS-SOM). On the learning method of neuron, we propose a new transformation method that adjust the neurons. Numerical examples and comparisons are given. These algorithms are also applied to real data with mixed feature data type. Finally, numerical results show that MFD-M-SOM has better performance than MFD-FS-SOM and MFD-FS-SOM is better than MFD-SOM.