The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform

Cai-xia Liu - One of the best experts on this subject based on the ideXlab platform.

  • E-CVFDT: An improving CVFDT method for concept drift data stream
    2013 International Conference on Communications Circuits and Systems (ICCCAS), 2013
    Co-Authors: Gang Liu, Hongrong Cheng, Zhiguang Qin, Qiao Liu, Cai-xia Liu
    Abstract:

    Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called Efficient CVFDT (E-CVFDT) is proposed to improve the efficiency of CVFDT. E-CVFDT introduces cache mechanism and treats the instances in three kinds of concept drift respectively, i.e. accidental concept drift, gradual concept drift, instantaneously concept drift. Besides, in E-CVFDT, the cached instances which have similar attributes will be sent in batches to calculate the Information Gain Calculation rather than in sequence adopted by CVFDT. The experiments are carried out on the MOA platform. The results show that E-CVFDT algorithm achieves not only better efficiency but also higher accuracy than CVFDT algorithm.

Huang Ai-hui - One of the best experts on this subject based on the ideXlab platform.

  • C4.5 Algorithm of Decision Tree Improvement and Application
    Science Technology and Engineering, 2009
    Co-Authors: Huang Ai-hui
    Abstract:

    According to C4.5 algorithm in the rate of Information Gain characteristics of the principle of using mathematical equivalent of the infinitesimal nature of a new algorithm to improve the C4.5,reduce the rate of Information Gain Calculation,thus improving C4.5 algorithm Information Gain Calculation of the rate of efficiency.Improved C4.5 algorithm compared with the original C4.5 algorithm,decision tree structure,with the same accuracy rate and a higher speed,will improve after the C4.5 algorithm applied to the analysis of results.

Tatsuya Harada - One of the best experts on this subject based on the ideXlab platform.

  • Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest.
    Frontiers in neuroinformatics, 2020
    Co-Authors: Xiaowei Zhang, Shaodi You, Shen Zhao, Zhenzhong Liu, Tatsuya Harada
    Abstract:

    One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Central Nervous System (CNS). In this paper, we present a novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data. We identify the key bottleneck of random forest to be the Information Gain Calculation and replace it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario. By properly modifying the training process of standard random forest, our algorithm significantly improves the performance while preserving the virtue of random forest such as low computational burden and robustness over over-fitting. Our method has shown a superior performance on both medical imaging analysis and machine learning benchmarks.

Shen Jing - One of the best experts on this subject based on the ideXlab platform.

  • The classification of LDA model essay based on Information Gain
    Journal of Chongqing University of Arts and Sciences, 2011
    Co-Authors: Shen Jing
    Abstract:

    In this paper the classification of short essay was improved based on LDA.The Information Gain of the essay with LDA classification method was put forward.Using the Information Gain Calculation to calculate the text classification vocabulary contribution,to improve "function word" weight,and to filter out "the function word",at last the passage of the filtered was in the LDA theme modeling,and the center vector method was used to establish the text category model.The experimental results prove that with the reducing of function word ratio,classification performance is distinctly improved in the method.

Gang Liu - One of the best experts on this subject based on the ideXlab platform.

  • E-CVFDT: An improving CVFDT method for concept drift data stream
    2013 International Conference on Communications Circuits and Systems (ICCCAS), 2013
    Co-Authors: Gang Liu, Hongrong Cheng, Zhiguang Qin, Qiao Liu, Cai-xia Liu
    Abstract:

    Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called Efficient CVFDT (E-CVFDT) is proposed to improve the efficiency of CVFDT. E-CVFDT introduces cache mechanism and treats the instances in three kinds of concept drift respectively, i.e. accidental concept drift, gradual concept drift, instantaneously concept drift. Besides, in E-CVFDT, the cached instances which have similar attributes will be sent in batches to calculate the Information Gain Calculation rather than in sequence adopted by CVFDT. The experiments are carried out on the MOA platform. The results show that E-CVFDT algorithm achieves not only better efficiency but also higher accuracy than CVFDT algorithm.