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    題名: The prediction approach with Growing Hierarchical Self-Organizing Map
    作者: Huang, S.-Y.;Tsaih, Ray
    蔡瑞煌
    貢獻者: 資管系
    關鍵詞: Classification mechanism;Classification performance;Competitive learning;Financial fraud;Financial reporting;Growing hierarchical self-organizing maps;Prediction rules;Salient features;Sample population;Training sample;Two classification;Unsupervised neural networks;Classification (of information);Conformal mapping;Crime;Detectors;Finance;Forecasting;Forestry;Neural networks;Sampling;Computer crime;Classification;Detectors;Finance;Forestry;Information Retrieval;Neural Networks;Sampling
    日期: 2012
    上傳時間: 2015-04-10 17:34:45 (UTC+8)
    摘要: The competitive learning nature of the Growing Hierarchical Self-Organizing Map (GHSOM), which is an unsupervised neural networks extended from Self-Organizing Map (SOM), can work as a regularity detector that is supposed to help discover statistically salient features of the sample population. With the spatial correspondent assumption, this study presents a prediction approach in which GHSOM is used to help identify the fraud counterpart of each non-fraud subgroup and vice versa. In this study, two GHSOMs a non-fraud tree (NFT) and a fraud tree (FT) are generated via the non-fraud samples and the fraud samples, respectively. Each (fraud or non-fraud) training sample is classified into its belonging leaf nodes of NFT and FT. Then, two classification rules are tuned based upon all training samples to determine the associated discrimination boundary within each leaf node, and the rule with better classification performance is chosen as the prediction rule. With the spatial correspondent assumption, the prediction rule derived from such an integration of FT and NFT classification mechanisms should work well. This study sets up the experiment of fraudulent financial reporting (FFR), a sub-field of financial fraud detection (FFD), to justify the effectiveness of the proposed prediction approach and the result is quite acceptable. © 2012 IEEE.
    關聯: Proceedings of the International Joint Conference on Neural Networks
    10.1109/IJCNN.2012.6252479
    資料類型: conference
    DOI 連結: http://dx.doi.org/10.1109/IJCNN.2012.6252479
    DOI: 10.1109/IJCNN.2012.6252479
    顯示於類別:[資訊管理學系] 會議論文

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