分類技術可以透過數據的特徵與相關性進行預測，因此在近年來得到ˋ越來越多的重視。其中，單類別分類通常使用於異常質檢測或是正負類別比例不平衡的數據。單調性分類問題則表示輸入的數據與某些特徵或決策標籤之間存在著單調性關係。而極限學習機(ELM)是分類問題中最常被使用的神經網路模型之一，透過隨機決定權重的特性，ELM能夠大大的減少計算時間，但是由於存在訓練誤差的問題，因此ELM並不是處理單調性分類問題的最佳模型。在過往的研究中，通常是透過增加單調性限制式來處理單調性分類問題，然而這樣的方式往往會增加運算的複雜度。有鑑於此，本研究希望能透過調整ELM的網路權重結構來滿足單調性限制。
本研究透過改變ELM網路權重的表達方式來滿足單調性限制，進而使ELM能夠妥善的處理單調性數據，此外我們生成一個決策函數來進行單類別的分類。透過實驗，可以發現我們所提出的模型在處理單調性數據分類問題上具有良好的穩定性。

Classification technology has become very popular since it can help make predictions through the characteristics and relevance of data. Among them, one-class classification is often used for outlier detection or for the data that is imbalance between positive and negative classes. Monotonic classification problems mean that there exists monotonicity relation between some features of input data and the decision label. Extreme learning machine (ELM) is one of the most commonly used network models in the classification problem. By the characteristic of randomly determining the weight, ELM can greatly reduce the computation time. However, because of the existence of training error, ELM is not a good model for monotonic classification problems. In the past, the monotonic classification was often carried out by adding monotonic constraints, but this would increase the complexity of the operation. Therefore, this study hopes to meets the monotonic constraints by adjust the network weight structure of ELM.
This study satisfied the monotonicity constraints by altering the expression of the network weights, so that ELM can handle the monotonic data. Furthermore, we generate a decision function to handle the problem of one-class classification. Through experiments, we find that the model we proposed has good stability in dealing with monotonic data classification problems.

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