Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
A field trial of 20 seed sources of Asparagus racemosus was conducted at the Forest Research Institute, Dehradun, Uttarakhand, India to evaluate their performance of different economic traits. Genotypic variance, phen...A field trial of 20 seed sources of Asparagus racemosus was conducted at the Forest Research Institute, Dehradun, Uttarakhand, India to evaluate their performance of different economic traits. Genotypic variance, phenotypic variance, genotypic coefficient of variance （GCV） and phenotypic coefficient of variance （PCV） for number of shoots, shoot height, shoot weight, number of roots, root length, root diameter and root weight were calculated. Maximum genotypic and phenotypic variance was observed in shoot height among the shoot - related traits and root length among the root - related traits. For the shoot height, genotypic variance, phenotypic variance, genotypic coefficient of variance, phenotypic coefficient of variance were 231.80, 3924.80, 61.26 and 1037.32, respectively, where those of the root length were 9.55, 16.80, 23.46 and 41.27, respectively. The maximum genetic advance and genetic gain were obtained for shoot height among the shoot-related traits and root length among the root-related traits. Index values were developed for all the seed sources based on the four most important traits, and Panthnagar （Uttrakhand）, Jodhpur （Rajasthan）, Dehradun （Uttarakhand）, Chandigarh （Punjab）, Jammu （Jammu and Kashmir） and Solan （Himachal Pradesh）, were promising seed sources for root production.展开更多
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
文摘A field trial of 20 seed sources of Asparagus racemosus was conducted at the Forest Research Institute, Dehradun, Uttarakhand, India to evaluate their performance of different economic traits. Genotypic variance, phenotypic variance, genotypic coefficient of variance （GCV） and phenotypic coefficient of variance （PCV） for number of shoots, shoot height, shoot weight, number of roots, root length, root diameter and root weight were calculated. Maximum genotypic and phenotypic variance was observed in shoot height among the shoot - related traits and root length among the root - related traits. For the shoot height, genotypic variance, phenotypic variance, genotypic coefficient of variance, phenotypic coefficient of variance were 231.80, 3924.80, 61.26 and 1037.32, respectively, where those of the root length were 9.55, 16.80, 23.46 and 41.27, respectively. The maximum genetic advance and genetic gain were obtained for shoot height among the shoot-related traits and root length among the root-related traits. Index values were developed for all the seed sources based on the four most important traits, and Panthnagar （Uttrakhand）, Jodhpur （Rajasthan）, Dehradun （Uttarakhand）, Chandigarh （Punjab）, Jammu （Jammu and Kashmir） and Solan （Himachal Pradesh）, were promising seed sources for root production.
