資料探勘技術能讓我們從大量資料中找出隱含的模式，進而萃取出有用的知識，其技術已被廣泛運用於分析與萃取知識信用貸款、破產預測，但是大部分資料探勘的研究皆為資料導向，但在實務運用上，可能會因為缺少企業智慧而降低決策品質，這造成學術與實務上巨大的鴻溝。而在實際生活中我們可以發現，屬性與類別存在著單調性的關係，利用這種性質，將先驗知識引入於分類模型中可以使分類正確率提高。
　　支援向量機(support vector machine, SVM)是近年來資料探勘熱門的工具之一，出色的學習能力成為目前機器學習研究焦點，在處理分類問題上已被廣泛應用。由於SVM是根據訓練實例來建構分類模型，會對於較不具重要性的資料或噪點過於重視及敏感，導致分類正確率下降。模糊理論概念的引入使模糊支援向量機可提供不同資料之不同的重要性，對於決策問題較具貢獻的資料應給予較高的貢獻值。
　　本研究提出知識導向於單調性來建構限制式，並利用各領域專家所提供的先備知識判斷資料中貢獻度，來建構知識導向具單調性限制式的模糊支援向量機模型。借助專家智慧，擷取資料集的單調性規則，接著進行資料前處理，並針對每筆資料給予不同的貢獻度，最後使用本研究提出的MC-FSVM模型來完成學習任務。經實驗證實，以具有單調性限制式及不同貢獻度的MC-FSVM模型在分類結果上，確實能有效增加分類器的成效，而且比傳統的SVM及FSVM模型好。

Data mining techniques, a part of Knowledge Discovery, is used to extract the hidden valuable information from large amounts of data. It is widely used for consumer loan evaluation and forecasting financial distress analysis. However, most of them are lack of business intelligence since they are data-driven and it causes a big gap between academic and business goal. In many real-world problems, we can see that there are some monotonicity relationships between the class and attributes and it has been shown that a classification technique incorporated with monotonicity constraints can improve accuracy.
Support vector machine (SVM) is a state-of-the-art artificial neural network based on statistical learning. The excellent ability is the focus of research in machine learning. However, some input points are more important to be fully assigned to one class to that SVM can separate these points more correctly. Some are corrupted by noises are less meaningful and the machine should discard them. Since importing the fuzzy theory, fuzzy SVM can provide different importance on the different information and give a higher membership to the information which is more contributions for decision-making problems.
In this study, we propose a knowledge-oriented new fuzzy support vector machine model with monotonicity constraints. Exploiting the experts knowledge to retrieve the monotonic rules from datasets. Constructing monotonicity constraints and determining the contribution of each information to implement the proposed classification model. The results of the experiments show that the proposed method, which considers the prior domain knowledge of monotonicity and different contribution of each data, performs better than the original SVM and FSVM model on classification problem.

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