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  • Active learning with an adaptive classifier for inaccessible big data analysis

    Author(s)
    Jahan, S
    Islam, MR
    Hasib, KM
    Naseem, U
    Islam, MS
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2021
    Metadata
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    Abstract
    Supervised machine learning (ML) approaches effectively derive valuable insights from big data. These approaches, on the other hand, require an extensive amount of high quality annotated data for training, created manually by domain experts through a costly and time-consuming process. To overcome this challenge, active learning (AL) is a promising approach, which can support a fast, cost-efficient and common strategy to deal with big data with limited labeling effort. Instead of annotating a large pool of unlabeled data, as in standard supervised learning, AL reduces the volume of data that requires manual annotation by ...
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    Supervised machine learning (ML) approaches effectively derive valuable insights from big data. These approaches, on the other hand, require an extensive amount of high quality annotated data for training, created manually by domain experts through a costly and time-consuming process. To overcome this challenge, active learning (AL) is a promising approach, which can support a fast, cost-efficient and common strategy to deal with big data with limited labeling effort. Instead of annotating a large pool of unlabeled data, as in standard supervised learning, AL reduces the volume of data that requires manual annotation by effectively selecting subsets of highly informative samples for manual annotation within an iterative process. In this paper, we aim to present a robust approach utilizing AL to mitigate the aforementioned challenges and help the decision-makers. To be precise, we propose a framework involving a support vector machine (SVM) technique in AL for mining big data to manage inaccessible data situations. The proposed approach is tested on five different semi-supervised data sets. The performance of the proposed framework is evaluated using traditional ML classifiers such as Naïve Bayes (NB), Decision Tree (DT), Sequential Minimal Optimization (SMO), Random Forest (RF), Bagging and Adaboost. Among the reported classifiers, bagging achieves the best outcome, delivering 99.19% accuracy. According to the results of the experiment conducted we find that the proposed method increases the efficiency of the classifiers in AL with fewer training instances.
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    Conference Title
    Proceedings of the International Joint Conference on Neural Networks
    Volume
    2021-July
    DOI
    https://doi.org/10.1109/IJCNN52387.2021.9534046
    Subject
    Data structures and algorithms
    Artificial intelligence not elsewhere classified
    Data mining and knowledge discovery
    Deep learning
    Training
    Support vector machines
    Radio frequency
    Annotations
    Supervised learning
    Manuals
    Big Data
    Publication URI
    http://hdl.handle.net/10072/411863
    Collection
    • Conference outputs

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