特徵選取的技術在資料探勘與機器學習的預處理程序扮演著相當重要的角色，一組優良的特徵集合在分類問題上不只可以擁有著高準確率並且可以減少探勘所需的時間。當訓練用的資料集合具備大量的特徵個數時，其所需要的計算時間通常是相當可觀。在這篇論文內，我們提出了三個基於基因演算法的屬性分群方法以做為特徵選取的技術。第一個方法使用正整數來進行染色體的編碼，並以每個基因值代表著這個特徵所歸屬的群聚。我們利用不同群聚內的特徵組合出來的集合之分類準確度與各群間特徵數量的平衡度來評估一個染色體的好壞。第二個方法則使用新的適應度函數來評估染色體的好壞，此新的適應度函數可大量減低掃描資料庫所需的時間。最後第三個方法改變原本的染色體編碼方式，不同基因存放不同群集的中心點，此新的編碼方式可以使基因演算法更快速的達到收斂速度。最後實驗將我們所提的方法與k-means所求得的分群結果做比較與討論。本篇論文所提出的三個演算法可在準確率與計算時間之間達成折衷。此外當使用傳統的特徵選取技術所得到的結果，若在推論時無法取得所選取特徵之數值時，則之前推導出的分類規則將無法被利用。然而利用我們所提出的演算法可以有效的解決特徵值遺失的情形，遺漏掉的特徵我們可以用與其相同的群集中的其他屬性來替換使用。因此我們所提出的特徵分群方法比以往的特徵選取技術有更大的彈性。

Feature selection is an important pre-processing step in mining and learning. A good set of features can not only improve the accuracy of classification, but also reduce the time to derive rules. It is executed especially when the amount of attributes in a given training data is very large. This thesis thus proposes three GA-based clustering methods for attribute clustering and feature selection. In the first method, each feasible clustering result is encoded into a chromosome with positive integers and a gene in the chromosome is for an attribute. The value of a gene represents the cluster to which the attribute belongs. The fitness of each individual is evaluated using both the average accuracy of attribute substitutions in clusters and the cluster balance. The second method further extends the first method to improve the time performance. A new fitness function based on both the accuracy and the attribute dependency is proposed. It can reduce the time of scanning the data base. The third approach uses another encoding method for representing chromosomes. It can achieve a faster convergence and a better result than the second one. At last, the experimental comparison with the k-means clustering approach and with all combinations of attributes also shows the proposed approach can get a good trade-off between accuracy and time complexity. Besides, after feature selection, the rules derived from only the selected features may usually be hard to use if some values of the selected features cannot be obtained in current environments. This problem can be easily solved in our proposed approaches. The attributes with missing values can be replaced by other attributes in the same clusters. The proposed approaches thus provide flexible alternatives for feature selection.

Chinese Abstract .................................................................................................... I
Eglissh Abstract .................................................................................................. III
CHAPTER 1 Introduction ................................................................................. 1
1.1 Background and Motivation ........................................................................................ 1
1.2 Contributions................................................................................................................ 4
1.3 Thesis Organization ..................................................................................................... 5
CHAPTER 2 Review of Related Works ............................................................ 6
2.1 The Concept of Clustering ........................................................................................... 6
2.2 Reduct .......................................................................................................................... 7
2.3 Genetic Algorithms ...................................................................................................... 9
CHAPTER 3 GA-Based Clustering with Accuracy Measurement .............. 12
3.1 Notation...................................................................................................................... 12
3.2 Chromosome Representation ..................................................................................... 13
3.3 Initial Population ........................................................................................................ 14
3.4 Fitness and Selection.................................................................................................. 14
3.5 Genetic Operators ...................................................................................................... 18
3.6 The Proposed Algorithm ............................................................................................ 21
3.7 An Example ................................................................................................................ 23
CHAPTER 4 GA-Based Clustering with Similarity ..................................... 29
4.1 Notation...................................................................................................................... 29
4.2 Fitness Function ......................................................................................................... 30
4.3 The Proposed Algorithm ............................................................................................ 34
4.4 An Example ................................................................................................................ 36
CHAPTER 5 GA-Based Clustering with a Novel Encoded Scheme ............ 42
5.1 Notation...................................................................................................................... 42
5.2 Chromosome Representation ..................................................................................... 43
5.3 Initial Population ........................................................................................................ 44
5.4 Fitness and Selection.................................................................................................. 45
5.5 Genetic Operators ...................................................................................................... 47
5.6 The Proposed Algorithm ............................................................................................ 47
5.7 An Example ................................................................................................................ 49
CHAPTER 6 Experimental Results ................................................................ 55
6.1 Experimental Results of Method(I) ........................................................................... 55
6.2 Experimental Results of Method(II) .......................................................................... 63
6.3 Experimental Results of Method(III) ......................................................................... 65
CHAPTER 7 Conclusions and Future Works ................................................ 68
References ........................................................................................................ 69

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