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The Role of Ensemble Learning in Stock Market Classification Model Accuracy Enhancement Based on Naive Bayes Classifiers

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  • Ghaith Abdulsattar A.Jabbar Alkubaisi

Abstract

Over the last years, methods of hybrid and ensemble have attracted the attention of the data mining community. Moreover, in the computational intelligence area such as machine learning, constructing and adaptive hybrid models have become essential to achieve good performance. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. Furthermore, there are many factors that have a direct effect on the classification models’ accuracies which were not addressed by previous research such as the automatic labelling technique which results in low classification accuracy due to the absence of specific lexicon, and the suitability of the classifiers to the data features and domain. In this research, a proposed model is designed to enhance the classification accuracy by the incorporation of stock market domain expert labelling technique and the construction of an ensemble Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of five phases. The first phase is data collection, and the second phase is labelling, in which polarity of data is specified and negative, positive or neutral values are assigned. The third phase involves data pre-processing. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by Ensemble Naïve Bayes classifiers, and the final is the performance and evaluation. The classification method has produced a significant result; it has achieved accuracy of more than 89%.

Suggested Citation

  • Ghaith Abdulsattar A.Jabbar Alkubaisi, 2020. "The Role of Ensemble Learning in Stock Market Classification Model Accuracy Enhancement Based on Naive Bayes Classifiers," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(1), pages 1-36, January.
  • Handle: RePEc:ibn:ijspjl:v:9:y:2020:i:1:p:36
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    References listed on IDEAS

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    1. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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