接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boos...接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boosting改进算法,通过在原始Boosting迭代中引入数据重平衡操作,实现弱学习算法优化目标从精度向AUC的迁移.实验结果表明,较之原始Boosting算法,新算法在AUC度量下能获得更好性能.展开更多
Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to p...Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to participate in the auction as a whole is helpful to increase purchasing power. However, SUs could suffer from a new group cheating problem, i.e., parts of users conspire to manipulate the auction by submitting untruthful bids. Existing auction mechanisms were mainly designed to be strategy-proof only for individual user and can't deal with group cheating. In this paper, a novel spectrum auction mechanism called COSTAG(COst Sharing based Truthful Auction with Group-buying) is proposed to address the group cheating problem. COSTAG consists of a grouping rule to perform grouping and a payment rule to determine the market-clearing price in the spectrum auction. Different from single-echelon pricing approach employed in existing works, a multi-echelon pricing strategy is designed to increase the transaction rate and optimize social profit for the auction. Comprehensive theoretical analysis shows that COSTAG can satisfy the crucial economic robustness properties, both individual and group truthfulness. Simulations demonstrate that comparing with existing works, COSTAG can improve the system performance significantly.展开更多
文摘接收者操作特性(Receiver operating characteristics,ROC)曲线下面积(Area under the ROC curve,AUC)常被用于度量分类器在整个类先验分布上的总体分类性能.原始Boosting算法优化分类精度,但在AUC度量下并非最优.提出了一种AUC优化Boosting改进算法,通过在原始Boosting迭代中引入数据重平衡操作,实现弱学习算法优化目标从精度向AUC的迁移.实验结果表明,较之原始Boosting算法,新算法在AUC度量下能获得更好性能.
基金partially supported by the National Science Foundation of China (No. 61070211, No. 61003304, No 61501482 and No 61070201)Equipment research foundation (No.6140134040216)the Ph.D. Programs Foundation of Ministry of Education of China (No. 20114307120003)
文摘Spectrum auction is an important approach of spectrum distribution in cognitive radio networks. However, a single secondary user(SU) probably can't afford the price of spectrum. Multiple SUs grouping together to participate in the auction as a whole is helpful to increase purchasing power. However, SUs could suffer from a new group cheating problem, i.e., parts of users conspire to manipulate the auction by submitting untruthful bids. Existing auction mechanisms were mainly designed to be strategy-proof only for individual user and can't deal with group cheating. In this paper, a novel spectrum auction mechanism called COSTAG(COst Sharing based Truthful Auction with Group-buying) is proposed to address the group cheating problem. COSTAG consists of a grouping rule to perform grouping and a payment rule to determine the market-clearing price in the spectrum auction. Different from single-echelon pricing approach employed in existing works, a multi-echelon pricing strategy is designed to increase the transaction rate and optimize social profit for the auction. Comprehensive theoretical analysis shows that COSTAG can satisfy the crucial economic robustness properties, both individual and group truthfulness. Simulations demonstrate that comparing with existing works, COSTAG can improve the system performance significantly.
