本文選題：支持向量數(shù)據(jù)描述 + 改進磷蝦群算法�。� 參考：《計算機工程與應用》2017年22期

【摘要】：支持向量數(shù)據(jù)描述(SVDD)是構(gòu)造單類數(shù)據(jù)描述的分類算法,懲罰參數(shù)C和核參數(shù)σ作為影響SVDD分類效果的關(guān)鍵,其合理選取一直是個難點。針對這一問題,提出了一種基于改進磷蝦群算法的SVDD參數(shù)優(yōu)化算法(IKH-SVDD)。依據(jù)仿真實驗,分析參數(shù)C和σ對描述邊界的影響;引入磷蝦群算法并分析其優(yōu)劣,通過在隨機擴散行為中定義擾動因子,增強算法的全局搜索能力;將一種新的精英選擇和保留策略引入迭代過程,提高算法的收斂精度;將改進的磷蝦群算法引入SVDD參數(shù)優(yōu)化過程,構(gòu)建了IKH-SVDD參數(shù)優(yōu)化模型。基于UCI標準數(shù)據(jù)庫進行實驗并與其他幾種參數(shù)優(yōu)化算法進行比較,結(jié)果表明了IKH-SVDD算法具有更高的分類準確性。
[Abstract]:Support vector data description (SVDDs) is a classification algorithm for constructing single-class data description. The penalty parameter C and kernel parameter 蟽 are the key to affect the classification effect of SVDD. The reasonable selection of SVDD classification is always a difficulty. In order to solve this problem, a SVDD parameter optimization algorithm based on improved krill swarm algorithm is proposed. According to the simulation experiment, the influence of parameters C and 蟽 on describing the boundary is analyzed, and the algorithm of krill swarm is introduced and its merits and demerits are analyzed. The global searching ability of the algorithm is enhanced by defining the disturbance factor in the random diffusion behavior. A new elite selection and retention strategy is introduced into the iterative process to improve the convergence accuracy of the algorithm, and the improved krill swarm algorithm is introduced into the optimization process of SVDD parameters, and the IKH-SVDD parameter optimization model is constructed. Experiments based on UCI standard database are carried out and compared with other parameter optimization algorithms. The results show that IKH-SVDD algorithm has higher classification accuracy.
【作者單位】： 空軍工程大學防空反導學院;
【基金】：國家自然科學基金(No.61273275)
【分類號】：TP18

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