模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
6期
502-508
,共7页
稀有类%簇%密度%特征权重%分离性
稀有類%簇%密度%特徵權重%分離性
희유류%족%밀도%특정권중%분리성
Rare Category%Cluster%Density%Feature Weight%Separability
稀有类挖掘是数据挖掘的一个重要研究领域,具有广泛的应用背景。文中针对传统稀有类识别算法存在的缺陷,提出一种基于密度差异与簇间分离性判据相结合的稀有类识别算法(RDACS)。该算法以特征权重相似度作为稀有类簇与周围数据样本间分离性的判据,并辅以积极学习的方法实现稀有类识别。在 UCI 公共数据集和KDD99数据集上的实验表明,与现有的同类算法相比,RDACS 在询问次数指标上有较明显优势,能提高效率并减少人为误差,是现有稀有类识别方法的一种补充算法。
稀有類挖掘是數據挖掘的一箇重要研究領域,具有廣汎的應用揹景。文中針對傳統稀有類識彆算法存在的缺陷,提齣一種基于密度差異與簇間分離性判據相結閤的稀有類識彆算法(RDACS)。該算法以特徵權重相似度作為稀有類簇與週圍數據樣本間分離性的判據,併輔以積極學習的方法實現稀有類識彆。在 UCI 公共數據集和KDD99數據集上的實驗錶明,與現有的同類算法相比,RDACS 在詢問次數指標上有較明顯優勢,能提高效率併減少人為誤差,是現有稀有類識彆方法的一種補充算法。
희유류알굴시수거알굴적일개중요연구영역,구유엄범적응용배경。문중침대전통희유류식별산법존재적결함,제출일충기우밀도차이여족간분리성판거상결합적희유류식별산법(RDACS)。해산법이특정권중상사도작위희유류족여주위수거양본간분리성적판거,병보이적겁학습적방법실현희유류식별。재 UCI 공공수거집화KDD99수거집상적실험표명,여현유적동류산법상비,RDACS 재순문차수지표상유교명현우세,능제고효솔병감소인위오차,시현유희유류식별방법적일충보충산법。
The rare category mining, which is an important research field in data mining, is widely applied. Aiming at the defects of the traditional rare category recognition methods, an rare category detection algorithm based on cluster separability ( RDACS), is proposed based on the combination of density difference and inter-cluster separability criterion for rare category mining. An active-learning scenario is used to detect rare category. The similarity of feature weight is applied to the separability of rare category cluster and its surrounding samples. The experimental results on UCI public datasets and KDD99 datasets show that compared with the existing similar algorithms, the RDACS algorithm has an advantage in the number of inquiries, which can significantly improve the efficiency and reduce human errors. RDACS is complementary to the existing rare category recognition methods.
