資料探勘可以從大量的資料中，挖掘出不易發現且有用的知識，協助決策者做出有效的決策，其中分類是資料探勘技術中重要的一環，分類是指根據已知資料的屬性建立分類模型，決策者利用此分類模型去預測新資料的類別，因為決策者不會盲目的相信自己沒有辦法理解的知識，所以分類模型不只要有高預測準確率，還必須同時具備高理解性，然而這兩個目標往往是彼此衝突的，沒有辦法找出同時滿足所有目標的單一最佳解，所以本研究希望找出多個目標之間的折衷最佳解集合。
　　近來粒子群最佳化演算法(Particle Swarm Optimizer, PSO)已經被成功地應用在解決單目標最佳化問題，且可以在很短的時間內得到非常不錯的結果，多目標粒子群最佳化演算法(Multi-Objective Particle Swarm Optimizer, MOPSO)就是其用來處理多目標問題的擴充模式，本研究將之使用於求解考慮預測準確率以及理解性兩目標的分類法則探勘問題，透過此演算法可獲得一非劣解(Non-Inferior Solution)集合，此一解集合可幫助決策者能於短時間內從龐大的可行解集合中挑選出最理想的分類模型。

　　Data mining can discover hidden and useful knowledge which can help decision makers to make a well-informed decision from large database. Classification is an important task for data mining. In classification, a model is built to assign a class to each instance in the dataset. Because decision makers could not blindly trust the incomprehensible output of the model, the classification model should not be only accurate but also comprehensible. However, accuracy and comprehensibility of classification model often conflict with each other. It is difficult to find out a solution to satisfy all the objectives. Therefore, this paper tries to find out a Pareto solution set.
　　Recently, the success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer has motivated researchers to extend the use of this bio-inspired technique to other areas. One of them is Multi-Objective Particle Swarm Optimizer (MOPSO). This paper uses MOPSO to evolve several classification models each having different prediction accuracy and comprehensibility. These models can make decision makers to easily choose the ideal decision alternatives.

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許志義。多目標決策。台北市：五南。（民83）