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H-MRST: A Novel Framework For Supporting Probability Degree Range Query Using Extreme Learning Machine

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Abstract

Background/Introduction

Data classification is an important application in the domain of cognitive computation, which has various applications. In this paper, we use classification techniques to solve some key issues in answering range query over probabilistic data. The key of answering this query is to store the feature of each uncertain object in a lightweight structure and use these structures for pruning/validating. However, in these works, the costly integral calculation has to be carried out when dealing with objects that cannot be pruned/validated, and some of the structure construction algorithms are not general.

Methods

In this paper, we employ ELM, a popular classification technique, to tackle the above issues. Our proposed methods are as follows: We firstly propose a new query called PDR (short for probabilistic degree range) query to substitute the traditional prob-range query, which helps us avoid the costly integral calculation. We propose an ELM-based “adapter” to construct the lightweight structure for uncertain data in a more general manner. We design the GO-ELM algorithm for answering PDR query. It first avoids most of the integral calculation via using a group of bit vector-based filter. In addition, we propose an ELM-based classifier, which is designed to further avoid integral operations.

Results

From the experiment results, we find that: (1) our ELM-based adapter is superior compared with both SVM-based and DNN-based adapter due to its better training efficiency and classification efficiency as well; (2) the performance of H-MRST is better than that of U-tree and UD-tree; and (3) ELM-filter could effectively avoid integral calculation.

Conclusions

This paper studies the problem of PDR query over uncertain data. We firstly define PDR query and propose a general scheme to handle uncertain object if its PDF is discrete. We then design GO-ELM algorithm for answering PDR query. Our experiments faithfully demonstrated the efficiency of our indexing techniques.

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Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (Nos. 61322208, 61572122, 61272178, 61532021, 01401256).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Bin Wang, Rui Zhu, Shiying Luo, Xiaochun Yang & Guoren Wang

Authors
  1. Bin Wang
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  2. Rui Zhu
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  3. Shiying Luo
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  4. Xiaochun Yang
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  5. Guoren Wang
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Correspondence to Bin Wang.

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Bin Wang, Rui Zhu, Shiying Luo, Xiaochun Yang and Guoren Wang declare that they have no conflict of interest.

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Informed consent was obtained from all individual participants included in the study.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Wang, B., Zhu, R., Luo, S. et al. H-MRST: A Novel Framework For Supporting Probability Degree Range Query Using Extreme Learning Machine. Cogn Comput 9, 68–80 (2017). https://doi.org/10.1007/s12559-016-9435-3

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  • DOI: https://doi.org/10.1007/s12559-016-9435-3

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