A Comparative Evaluation of Meta Classification Algorithms with Smokers Lung Data

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Data mining is the course of process during which knowledge is extracted through interesting patterns recognized from large amount of data. It is one of the knowledge exploring areas which is widely used in the field of computer science. Data mining is an inter-disciplinary area which has great impact on various other fields such as data analytics in business organizations, medical forecasting and diagnosis, market analysis, statistical analysis and forecasting, predictive analysis in various other fields. Data mining has multiple forms such as text mining, web mining, visual mining, spatial mining, knowledge mining and distributed mining. In general the process of data mining has many tasks from pre-processing. The actual task of data mining starts after the preprocessing task. This work deals with the analysis and comparison of the various Data mining algorithms particularly Meta classifiers based upon performance and accuracy. This work is under medical domain, which is using the lung function test report data along with the smoking data. This medical data set has been created from the raw data obtained from the hospital. In this paper work, we have analyzed the performance of Meta classifiers for classifying the files. Initially the performances of Meta and Rule classifiers are analyzed observed and found that the Meta classifier is more efficient than the Rule classifiers in Weka tool. The implementation work then continued with the performance comparison between the different types of classification algorithm among which the Meta classifiers showed comparatively higher accuracy in the process of classification. The four Meta classifier algorithms which are widely explored using the Weka tool namely Bagging, Attribute Selected Classifier, Logit Boost and Classification via Regression are used to classify this medical dataset and the result so obtained has been evaluated and compared to recognize the best among the classifier.



  • Keywords

    Data mining, Weka, meta classifier, lung function test, bagging, attribute selected classifier, logit boost, classification via Regression.

  • References

      [1] Rohini K & Suseendran G, “Aggregated K Means Clustering and Decision Tree Algorithm for Spirometer Data”, Indian Journal of Science and Technology, Vol.9, No.44, (2016), pp.1-6.

      [2] Srivastava S, “Weka: a tool for data preprocessing, classification, ensemble, clustering and association rule mining”, International Journal of Computer Applications, Vol.88, No.10, (2014), pp.26-29.

      [3] Khatri MD & Dhande S, “History and Current and Future trends of Data mining Techniques”, International Journal of Advance Research in Computer Science and Management Studies, Vol.2, No.3, (2014), pp.311-315.

      [4] Rohini K & Suseendran G, “Predicting lung disease severity evaluation and comparison of hybrid decision tree algorithm”, Indian Journal of Innovations and Developments Vol.6, No.1, (2017), pp.1-15.

      [5] Ian HW, Eibe F & Mark AH, Data Mining Practical Machine Learning Tools and Techniques. 3rd Edition, Elsevier, (2011).

      [6] Breiman L, “Bagging predictors”, Machine Learning, Vol.24, No.2, (1996) 123–140.

      [7] Cohen WW, “Fast effective rule induction”, Twelfth International Conference on Machine Learning, (1995), pp.115–123.

      [8] Quinlan J, C4.5: programs for machine learning. San Mateo, CA: Morgan Kaufmann, 1993.

      [9] Demiroz G & Guvenir A, “Classification by voting feature intervals”, Ninth European Conference on Machine Learning, (1997), pp.85–92.

      [10] Chye K & Gerald T, “Data Mining Applications in Healthcare”, Journal of Healthcare Information Management, Vol.19, No.2, (2011), pp.64-72.




Article ID: 24543
DOI: 10.14419/ijet.v7i4.36.24543

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.