计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2015年
4期
952-955,976
,共5页
标准粒子群%混沌粒子群%特征选择%数据分类%初始潜能%动态惯性权重%早熟判断
標準粒子群%混沌粒子群%特徵選擇%數據分類%初始潛能%動態慣性權重%早熟判斷
표준입자군%혼돈입자군%특정선택%수거분류%초시잠능%동태관성권중%조숙판단
standard particle swarm%chaos particle swarm%feature selection%data classification%initial latent energy%dynami-cal inertia weight%premature judgment
为解决数据维数高、信息冗余导致的数据处理问题，提出基于改进混沌粒子群的最优特征提取方法。引入初始潜能的概念优化粒子群的初始化，降低传统的随机初始化导致的盲目性；在此基础上同时考虑粒子的适应度值和位置两个影响因素，对权重做出动态调整，调节空间范围搜索的能力，通过早熟判断机制，及时引入混沌变量避免局部最优。KDD-CUP99数据实验得到的分类正确率验证了该方法的有效性和高效性。
為解決數據維數高、信息冗餘導緻的數據處理問題，提齣基于改進混沌粒子群的最優特徵提取方法。引入初始潛能的概唸優化粒子群的初始化，降低傳統的隨機初始化導緻的盲目性；在此基礎上同時攷慮粒子的適應度值和位置兩箇影響因素，對權重做齣動態調整，調節空間範圍搜索的能力，通過早熟判斷機製，及時引入混沌變量避免跼部最優。KDD-CUP99數據實驗得到的分類正確率驗證瞭該方法的有效性和高效性。
위해결수거유수고、신식용여도치적수거처리문제，제출기우개진혼돈입자군적최우특정제취방법。인입초시잠능적개념우화입자군적초시화，강저전통적수궤초시화도치적맹목성；재차기출상동시고필입자적괄응도치화위치량개영향인소，대권중주출동태조정，조절공간범위수색적능력，통과조숙판단궤제，급시인입혼돈변량피면국부최우。KDD-CUP99수거실험득도적분류정학솔험증료해방법적유효성화고효성。
To solve data processing problem caused by high dimensionality of data and information redundancy,optimal features extraction method was proposed based on improved chaos particle swarm.Firstly,the particle position was initialized through in-troducing the concept of initial potential,and the blindness caused by traditional random initialization was reduced.On this basis, the influence of two factors of fitness values and location was taken into account,and weight was adjusted dynamically to adjust the scope of ability to search space.Then chaotic variables were introduced by the premature judgment mechanism to avoid local optima.The classification accuracy of KDDCUP99 data obtained experimentally verifies the effectiveness and efficiency of the presented method.
