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Conference Papers Year : 2019

Iliou Machine Learning Data Preprocessing Method for Suicide Prediction from Family History

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Theodoros Iliou
  • Function : Author
  • PersonId : 1033525
Georgia Konstantopoulou
  • Function : Author
  • PersonId : 1033526
Christina Lymperopoulou
  • Function : Author
  • PersonId : 1056790
Konstantinos Anastasopoulos
  • Function : Author
  • PersonId : 1033527
Dimitrios Margounakis
  • Function : Author
  • PersonId : 1056791
Dimitrios Lymberopoulos
  • Function : Author
  • PersonId : 1008213

Abstract

As real world data tends to be incomplete, noisy and inconsistent, data preprocessing is an important issue for data mining. Data preparation includes data cleaning, data integration, data transformation and data reduction. In this paper, Iliou preprocessing method is compared with Principal Component Analysis in suicide prediction according to family history. The dataset consists of 360 students, aged 18 to 24, who were experiencing family history problems. The performance of Iliou and Principal Component Analysis data preprocessing methods was evaluated using the 10-fold cross validation method assessing ten classification algorithms, IB1, J48, Random Forest, MLP, SMO, JRip, RBF, Naïve Bayes, AdaBoostM1 and HMM, respectively. Experimental results illustrate that Iliou data preprocessing algorithm outperforms Principal Component Analysis data preprocessing method, achieving 100% against 71.34% classification performance, respectively. According to the classification results, Iliou preprocessing method is the most suitable for suicide prediction.
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Dates and versions

hal-02331310 , version 1 (24-10-2019)

Licence

Attribution - CC BY 4.0

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Theodoros Iliou, Georgia Konstantopoulou, Christina Lymperopoulou, Konstantinos Anastasopoulos, George Anastassopoulos, et al.. Iliou Machine Learning Data Preprocessing Method for Suicide Prediction from Family History. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.512-519, ⟨10.1007/978-3-030-19823-7_43⟩. ⟨hal-02331310⟩
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