資料挖掘技術已被許多企業廣泛應用，以從大量雜亂的商業資料中，得到洞察先機的機會。類神經網路是目前資料挖掘常用的技術之一，更已經被廣泛地應用在其他問題領域中，例如：操控交通工具，辨識DNA序列，貨運空間配置的排列，以及預測匯率等。雖然，類神經網路在這些監督式和非監督式的學習問題上，已經有許多成功應用的實例，但當考慮直接將類神經網路應用於資料挖掘問題時，就必須面對兩個重大的問題，那就是從大量資料中形成模式所需要的時間和學習模式的可理解性。所訓練得到的模式不易被理解，是類神經網路最常受到的批評，但近年來學者們已經發展出許多可行的解決方案，如：Shavlik 與Lu就提出十分成功的方法，以從完成訓練的類神經網路中萃取規則。可是，相對的，針對類神經網路必須耗費許多訓練時間以處理大量資料的改善方案與研究，卻相當的缺乏。本研究即針對此問題，應用詢問式倒傳遞類神經網路學習方法於資料挖掘的分類問題上，以解決傳統類神經網路在處理大量資料時必須耗費許多時間的問題。詢問式學習法的主要精神，在於針對學習者不清楚的地方加強學習，正如孔子所言：「因才施教」，所以往往能事半功倍。本研究的目的是利用詢問式的學習方法訓練倒傳遞類神經網路，以增進網路的學習效率，並提高模式的正確性。本論文考慮多項資料挖掘的應用問題，包括：心臟病、乳癌、糖尿病之疾病診斷資料與電子商務之人口統計資料。並考慮類神經網路模式的學習效率、預測正確率、預測可靠度，以包括假說判斷表(Contingency table)與接受者操作特徵曲線(Receiver Operating Characteristic Curve, ROC)等多項重要指標，來進行驗證。實驗結果顯示，不論在訓練時間和分類的結果，我們的詢問式倒傳遞類神經網路，都明顯的比原始倒傳遞類神經網路為佳。未來我們希望擴展詢問式的學習方法到其他資料挖掘技術上。

The central focus of data mining in enterprises is to gain insight into large collections of data for making a good prediction and a right decision. Neural networks have been applied to a wide variety of problem domains such as steering motor vehicles, recognizing genes in uncharacterized DNA sequences, scheduling payloads for the space shuttle, and predicting exchange rates. Advantages of neural networks include the high tolerance to noisy data as well as the ability to classify patterns having not been trained. Neural networks have been successfully applied to a wide range of supervised and unsupervised learning problems. However, while being applied in data mining, there are two fundamental considerations - the comprehensibility of learned models and the time required to induce models from large data sets. For the first problem, many approaches have been proposed for extracting rules from trained neural networks. In this thesis, we focus on the second problem. We introduce a query-based learning algorithm to improve neural networks'' performance in data mining. Results show that the proposed algorithm can significantly reduce the training set cardinality. Our future work is to apply this learning procedure to other data mining schemes.

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