计算机工程与设计
計算機工程與設計
계산궤공정여설계
Computer Engineering and Design
2015年
11期
3000-3004
,共5页
多分类器集成%分类%AdaBoost%动态选择%属性相关度
多分類器集成%分類%AdaBoost%動態選擇%屬性相關度
다분류기집성%분류%AdaBoost%동태선택%속성상관도
ensemble of classifiers%classification%AdaBoost%dynamic selection%attribute correlation
为提高分类准确率，研究一种改进的多分类器动态集成算法。调整AdaBoost ，使其适用于加权训练集；引入属性相关度来标记待分类样本和训练集决策属性之间的相似程度，实现以动态筛选的方式组合最终的分类模型。该算法避免了在分类模型集成过程中对训练集的重复抽取，弥补了模型中单分类器位置固定不变的不足。实验结果表明，该算法能有效提高分类精度和泛化能力。
為提高分類準確率，研究一種改進的多分類器動態集成算法。調整AdaBoost ，使其適用于加權訓練集；引入屬性相關度來標記待分類樣本和訓練集決策屬性之間的相似程度，實現以動態篩選的方式組閤最終的分類模型。該算法避免瞭在分類模型集成過程中對訓練集的重複抽取，瀰補瞭模型中單分類器位置固定不變的不足。實驗結果錶明，該算法能有效提高分類精度和汎化能力。
위제고분류준학솔，연구일충개진적다분류기동태집성산법。조정AdaBoost ，사기괄용우가권훈련집；인입속성상관도래표기대분류양본화훈련집결책속성지간적상사정도，실현이동태사선적방식조합최종적분류모형。해산법피면료재분류모형집성과정중대훈련집적중복추취，미보료모형중단분류기위치고정불변적불족。실험결과표명，해산법능유효제고분류정도화범화능력。
To improve the accuracy rate of classification ,an improved dynamic integration algorithm of multiple classifiers was studied .The AdaBoost algorithm was redefined ,so that it was applicable to the weighted training set .The definition of the at‐tribute correlation between the sample to be tested and decision attributes of the training set was introduced ,and the final classi‐fication model was assembled by means of dynamic selection .The improved algorithm avoids re‐sampling of training sets ,and re‐solves the problem that the improved AdaBoost generates the aptotic array of classifiers to all the samples .Experimental results show that the proposed algorithm effectively improves the classification precision ,and gets better classification results .
