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Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

Symmetry (Basel), 2020-10, Vol.12 (10), p.1620 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2073-8994 ;EISSN: 2073-8994 ;DOI: 10.3390/sym12101620

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  • Title:
    Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model
  • Author: Chen, You-Shyang ; Sangaiah, Arun Kumar ; Chen, Su-Fen ; Huang, Hsiu-Chen
  • Subjects: advanced multicomponential discretization models ; Big Data ; Book value ; Capital expenditures ; Cash flow statements ; Classification ; Commodities ; Corporate profits ; Data mining ; Data science ; data-discretization methods ; data-mining techniques ; Decision trees ; Discounted cash flow ; Discretization ; Dividends ; Earnings per share ; feature-selection methods ; Financial analysis ; Financial statements ; Hybrid systems ; industry data applications ; Interest rates ; Investments ; Machine learning ; Market potential ; Market prices ; Model accuracy ; Model testing ; Money markets ; Present value ; Profitability ; Securities markets ; Stock exchanges ; Trends
  • Is Part Of: Symmetry (Basel), 2020-10, Vol.12 (10), p.1620
  • Description: Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2073-8994
    EISSN: 2073-8994
    DOI: 10.3390/sym12101620
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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