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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/111624
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111624

    Title: Heterogeneous AdaBoost with stochastic algorithm selection
    Authors: Hsu, Kuo-Wei
    Contributors: 資訊科學系
    Keywords: Adaptive boosting;Classification (of information);Information management;Random processes;Stochastic systems;Algorithm selection;Boosting;Ensemble;Ensemble learning;Ensemble learning algorithm;Stochastic algorithms;Training data sets;Learning algorithms
    Date: 2017-01
    Issue Date: 2017-08-03 14:13:12 (UTC+8)
    Abstract: We propose an ensemble learning algorithm based on AdaBoost and employing heterogeneous algorithms with a stochastic process for algorithm selection. Diversity is an important factor in ensemble learning and AdaBoost creates diversity by manipulating training data sets. However, we observe that AdaBoost generates training data sets of low diversity in later iterations. Some researchers suggest the employment of heterogeneous algorithms in ensemble learning to achieve better diversity. Following the idea, we extend AdaBoost and propose an algorithm that employs different base learning algorithms in different iterations. The most distinguishing feature of our algorithm is that it selects algorithms using a stochastic process where their earlier performance is considered. The results from experiments on several data sets show the utility of our algorithm: It could outperform AdaBoost on 22 to 33, depending on settings, out of 40 data sets considered in experiments. © 2017 ACM.
    Relation: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017,
    11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017; Beppu; Japan; 5 January 2017 到 7 January 2017; 代碼 126221
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1145/3022227.3022266
    DOI: 10.1145/3022227.3022266
    Appears in Collections:[資訊科學系] 會議論文

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