计算机系统应用
計算機繫統應用
계산궤계통응용
APPLICATIONS OF THE COMPUTER SYSTEMS
2014年
11期
92-98
,共7页
近邻分类%变精度粗糙集%代表点%分类模型%上%下近似
近鄰分類%變精度粗糙集%代錶點%分類模型%上%下近似
근린분류%변정도조조집%대표점%분류모형%상%하근사
nearest neighbor classification%variable precision rough set%representative%classification model%upper and lower approximation
RSKNN 算法是一种基于变精度粗糙集理论的 k-近邻改进算法，该算法能够保证在一定分类精度的前提下，有效地降低分类的计算量，提高分类效率。但由于 RSKNN 算法只是简单地将每个类中的样本划分成一个核心和边界区域，并没有根据数据集本身的特点进行划分，因而存在极大的局限性。针对存在的问题，提出一种多代表点学习算法，运用结构风险最小化理论对影响分类模型期望风险的因素进行分析，并使用无监督的局部聚类算法学习优化代表点集合。在UCI公共数据集上的实验表明，该算法比RSKNN算法具有更高的分类精度。
RSKNN 算法是一種基于變精度粗糙集理論的 k-近鄰改進算法，該算法能夠保證在一定分類精度的前提下，有效地降低分類的計算量，提高分類效率。但由于 RSKNN 算法隻是簡單地將每箇類中的樣本劃分成一箇覈心和邊界區域，併沒有根據數據集本身的特點進行劃分，因而存在極大的跼限性。針對存在的問題，提齣一種多代錶點學習算法，運用結構風險最小化理論對影響分類模型期望風險的因素進行分析，併使用無鑑督的跼部聚類算法學習優化代錶點集閤。在UCI公共數據集上的實驗錶明，該算法比RSKNN算法具有更高的分類精度。
RSKNN 산법시일충기우변정도조조집이론적 k-근린개진산법，해산법능구보증재일정분류정도적전제하，유효지강저분류적계산량，제고분류효솔。단유우 RSKNN 산법지시간단지장매개류중적양본화분성일개핵심화변계구역，병몰유근거수거집본신적특점진행화분，인이존재겁대적국한성。침대존재적문제，제출일충다대표점학습산법，운용결구풍험최소화이론대영향분류모형기망풍험적인소진행분석，병사용무감독적국부취류산법학습우화대표점집합。재UCI공공수거집상적실험표명，해산법비RSKNN산법구유경고적분류정도。
RSKNN is an improved kNN algorithm based on variable parameter rough set model. The algorithm guarantees under the premise of a certain classification accuracy, effectively reduces the computation burden of the classified samples, and improves the computation efficiency and precision of classification. But in this algorithm ,the instances of each class are simply classified into core and boundary areas. It has the limitation that it isn’t classified according the features of datasets. An efficient algorithm aiming at learning multi-representatives for RSKNN is proposed. Using the theory of structural risk minimization, a few factors that determine the expected risk of new classification model are analyzed. And an unsupervised algorithm for partial clustering is used to build an optimal set of representatives. Experimental results on UCI public datasets demonstrate that the proposed method significantly improves the accuracy of the classification.
