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Bagging and Boosting Ensembles for Conflict Resolution on Heterogeneous Data

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Intelligent Computing and Optimization (ICO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1072))

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Abstract

In the era of big data and with the advent of the Internet of things (IoT) more and more of devices are being connected to internet and are sending voluminous amounts of data. The potential of this huge volume of unconnected data remains untapped. It poses a greater challenge to generate insights from this dark data at all levels in the data mining process i.e. from pre-processing of data to reports generation. As such the quality and reliability of data is of utmost importance. One of the challenges addressed in this paper is challenge of truth discovery or veracity of the data. Data veracity estimation is a challenging concept be it in Internet of things (IoT) or wireless sensor networks (WSN). In IoT it is achieved at computational level whereas in WSN it is achieved at network level. When there are multiple conflicting information sources generating data, we have to find out a way to ascertain the correct value and provide a source reliability index for each and every source. Though there are a number of truth discovery algorithms in literature a major challenge lies in determining which method to select and the performance evaluation of the method given the limited availability of ground truth values. In this paper we propose two algorithms using bootstrapped aggregation (Bagging) technique and Boosting technique to arrive at the results on a weather data set. The weather data set chosen here consists of continuous as well as categorical values (Heterogeneous data) and both have been handled as part of this algorithm.

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Acknowledgment

We wish to acknowledge the Department of Science and Technology, India and School of Computing, Sathyabama Institute of science and Technology, Chennai for providing the facilities to do the research under the DST-FIST Grant Project No. SR/FST/ETI- 364/2014.

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    Adilakshmi Vadavalli & Subhashini Radhakrishnan

Authors
  1. Adilakshmi Vadavalli
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  2. Subhashini Radhakrishnan
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Corresponding author

Correspondence to Subhashini Radhakrishnan .

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Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Pandian Vasant

  2. Computer Science, FEI, VSB-TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

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Vadavalli, A., Radhakrishnan, S. (2020). Bagging and Boosting Ensembles for Conflict Resolution on Heterogeneous Data. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-33585-4_43

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