CAJ | 학술논문

针对传统的支持向量数据描述( SVDD)因未考虑数据构成的多模态性和局部分布的非同一性,难以获取目标数据的优化决策边界,所建立的数学模型难以正确反映建模对象的时空变化规律的问题,提出一种基于局部优化边界的支持向量数据描述( LOB-SVDD)方法。通过求取局部数据样本的分散程度获取支持向量机算法中折衷参数的局部调整系数,以此优化求解决策边界函数,由此可实现数据分类、离群点检测和数据建模等。利用UCI 数据集和人工双模态数据集进行的仿真表明,与传统方法相比,LOB-SVDD可获得更优的决策边界,作为分类器有更低的假正率和假负率。应用LOB-SVDD对具有多模态特性的铜锍吹炼实际生产数据进行预处理,能有效检测离群点,剔除异常样本,实现数据洁净化。
침대전통적지지향량수거묘술( SVDD)인미고필수거구성적다모태성화국부분포적비동일성,난이획취목표수거적우화결책변계,소건립적수학모형난이정학반영건모대상적시공변화규률적문제,제출일충기우국부우화변계적지지향량수거묘술( LOB-SVDD)방법。통과구취국부수거양본적분산정도획취지지향량궤산법중절충삼수적국부조정계수,이차우화구해결책변계함수,유차가실현수거분류、리군점검측화수거건모등。이용UCI 수거집화인공쌍모태수거집진행적방진표명,여전통방법상비,LOB-SVDD가획득경우적결책변계,작위분류기유경저적가정솔화가부솔。응용LOB-SVDD대구유다모태특성적동류취련실제생산수거진행예처리,능유효검측리군점,척제이상양본,실현수거길정화。
Conventional support vector data description ( SVDD) , which did not consider multi-modal and local distribution difference of the data, failed to reflect time-space variety rule of the object and hard to gain the optimal decision boundary. To solve this difficulty, a new SVDD method with local optimization boundary ( LOB-SVDD) was proposed. First, the local dispersion degree of each data point was calculat-ed, then, the coefficient of trade-off parameters was adjusted with the local dispersion degree, finally, the quadratic programming problem was solved and an optimized boundary function was obtained. The method can be used in data classification, outlier detection and data modeling, etc. Experiments with UCI datasets and artificial dual mode datasets show that the method can gain a more optimal decision boundary compared to the conventional method, and as classifier it can gain lower false positives rate and false nega-tives rate. That method was applied to the multi-modal actual production data of copper matte converting process, and the results show that it can effectively detect outliers, eliminate abnormal sample data.