The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class cla...The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.展开更多
A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,t...A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.展开更多
Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules.The paper gives the method to encode for the rules,the fitness funct...Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules.The paper gives the method to encode for the rules,the fitness function,the selecting,crossover,mutation and migration operator for the DAGA at the same time are designed.展开更多
基金supported by the National Natural Science Foundation of China(61703131 61703129+1 种基金 61701148 61703128)
文摘The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.
基金Sponsored by the National Natural Science Foundation of China (60773129)the Excellent Youth Science and Technology Foundation of Anhui Province of China ( 08040106808)
文摘A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.
基金National Ethnic Affairs Commission NatureScience Foundation of China(PMZY06004)the Education Science Foundation of Guangxi(2006A-E004)
文摘Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules.The paper gives the method to encode for the rules,the fitness function,the selecting,crossover,mutation and migration operator for the DAGA at the same time are designed.
