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標題: Application of territorial defense based Lion's Algorithms for Feature Selection with Support Vector Machine Classifiers
作者: 魏振庭
Jhen-ting Wei
關鍵字: feature selection
lion's algorithm
support vector machine
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UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science.
摘要: Classification problem is one of the applications of machine learning. Because of the numbers and dimensions of data increasing, the performance of classification algorithm is not well. Feature selection can not only remove those features which are not corresponding to the classification but find the best feature subset which can let classifier perform much better. In this study, we use bio-inspired algorithms to do feature selection, and we use SVM as a classifier. In this study, we use lion's algorithm, a novel bio-inspired algorithm developed in 2012, as our algorithm to do feature selection. In our lab, there was a modified version developed before. In this study, a new modified version based on the past paper in our lab will be introduced. Moreover, the mix version of two versions above will be developed. In the experiment part, we will compare the performance of those different modified versions of lion's algorithm.
在機器學習的領域中,分類問題是一種常見的應用。但隨著資料量大量增加與資料維度的上升,演算法面臨效能低落的問題。透過特徵選取,能有效去除多餘、部相關甚至對分類有害的特徵,藉此改善分類效能。因此我們透過仿生演算法則來當作特徵選取的工具,以支援向量機作為分類器來驗證分類效能。 在本研究中,我們採用的仿生演算法是在2012年所發表的獅子演算法,乃是模仿獅子社會行為的仿生演算法,主要藉由形成獅群、繁衍後代、領土防禦、領土爭奪四大步驟來來演化求得最佳解。本實驗室過去已有針對獅子演算法進行改良的研究,其改良方式為增加全鄰域搜索步驟於領土爭奪處。本研究以此為基礎,提出自己的改良方法,乃是改良領土的防禦步驟進而提升獅子演算法的搜尋效率。本研究將比較原始獅子演算法、過去由本實驗室提出的改良型、本研究的改良型與結合前面兩者的綜合型的分類效能,實驗採用UCI資料及作為訓練及測試資料。
其他識別: U0005-0608201515401300
文章公開時間: 2018-08-19
Appears in Collections:資訊管理學系



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