Miner for OACCR: Case of medical data analysis in knowledge discovery

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@InProceedings{Ali:2012:SETIT,
  author =       "Samaher Hussein Ali",
  booktitle =    "6th International Conference on Sciences of
                 Electronics, Technologies of Information and
                 Telecommunications (SETIT 2012)",
  title =        "Miner for OACCR: Case of medical data analysis in
                 knowledge discovery",
  year =         "2012",
  pages =        "962--975",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 medical administrative data processing, OACCR, TreeNet
                 classifier, astroinformatics, bioinformatics, data
                 mining algorithm, datasets, genetic programming data
                 construction method, geoinformatics, hybrid techniques,
                 knowledge discovery, medical data analysis, obtaining
                 accurate and comprehensible classification rules,
                 principle component analysis, scientific World Wide
                 Web, Algorithm design and analysis, Classification
                 algorithms, Clustering algorithms, Data mining,
                 Databases, Training, Vegetation, Adboosting, FP-Growth,
                 GPDCM, PCA, Random Forest",
  DOI =          "doi:10.1109/SETIT.2012.6482043",
  size =         "14 pages",
  abstract =     "Modern scientific data consist of huge datasets which
                 gathered by a very large number of techniques and
                 stored in much diversified and often incompatible data
                 repositories as data of bioinformatics, geoinformatics,
                 astroinformatics and Scientific World Wide Web. At the
                 other hand, lack of reference data is very often
                 responsible for poor performance of learning where one
                 of the key problems in supervised learning is due to
                 the insufficient size of the training dataset.
                 Therefore, we try to suggest a new development a
                 theoretically and practically valid tool for analysing
                 small of sample data remains a critical and challenging
                 issue for researches. This paper presents a methodology
                 for Obtaining Accurate and Comprehensible
                 Classification Rules (OACCR) of both small and huge
                 datasets with the use of hybrid techniques represented
                 by knowledge discovering. In this article the searching
                 capability of a Genetic Programming Data Construction
                 Method (GPDCM) has been exploited for automatically
                 creating more visual samples from the original small
                 dataset. Add to that, this paper attempts to developing
                 Random Forest data mining algorithm to handle missing
                 value problem. Then database which describes depending
                 on their components were built by Principle Component
                 Analysis (PCA), after that, association rule algorithm
                 to the FP-Growth algorithm (FP-Tree) was used. At the
                 last, TreeNet classifier determines the class under
                 which each association rules belongs to was used. The
                 proposed methodology provides fast, Accurate and
                 comprehensible classification rules. Also, this
                 methodology can be use to compression dataset in two
                 dimensions (number of features, number of records).",
  notes =        "Also known as \cite{6482043}",
}

Genetic Programming entries for Samaher Hussein Ali

Citations