Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences

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  author =       "Safdar Ali and Abdul Majid",
  title =        "{Can-Evo-Ens}: Classifier stacking based evolutionary
                 ensemble system for prediction of human breast cancer
                 using amino acid sequences",
  journal =      "Journal of Biomedical Informatics",
  volume =       "54",
  pages =        "256--269",
  year =         "2015",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Breast
                 cancer, Amino acids, Physicochemical properties,
                 Stacking ensemble",
  ISSN =         "1532-0464",
  DOI =          "doi:10.1016/j.jbi.2015.01.004",
  URL =          "",
  abstract =     "The diagnostic of human breast cancer is an intricate
                 process and specific indicators may produce negative
                 results. In order to avoid misleading results, accurate
                 and reliable diagnostic system for breast cancer is
                 indispensable. Recently, several interesting
                 machine-learning (ML) approaches are proposed for
                 prediction of breast cancer. To this end, we developed
                 a novel classifier stacking based evolutionary ensemble
                 system Can-Evo-Ens for predicting amino acid sequences
                 associated with breast cancer. In this paper, first, we
                 selected four diverse-type of ML algorithms of Naive
                 Bayes, K-Nearest Neighbour, Support Vector Machines,
                 and Random Forest as base-level classifiers. These
                 classifiers are trained individually in different
                 feature spaces using physicochemical properties of
                 amino acids. In order to exploit the decision spaces,
                 the preliminary predictions of base-level classifiers
                 are stacked. Genetic programming (GP) is then employed
                 to develop a meta-classifier that optimal combine the
                 predictions of the base classifiers. The most suitable
                 threshold value of the best-evolved predictor is
                 computed using Particle Swarm Optimisation technique.
                 Our experiments have demonstrated the robustness of
                 Can-Evo-Ens system for independent validation dataset.
                 The proposed system has achieved the highest value of
                 Area Under Curve (AUC) of ROC Curve of 99.95percent for
                 cancer prediction. The comparative results revealed
                 that proposed approach is better than individual ML
                 approaches and conventional ensemble approaches of
                 AdaBoostM1, Bagging, GentleBoost, and Random Subspace.
                 It is expected that the proposed novel system would
                 have a major impact on the fields of Biomedical,
                 Genomics, Proteomics, Bioinformatics, and Drug

Genetic Programming entries for Safdar Ali Abdul Majid