Search Results - (( java implementation mead algorithm ) OR ( using evolutionary mining algorithm ))

  • Showing 1 - 15 results of 15
Refine Results
  1. 1

    Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin by Latiffah, A. Noor, Nordin, A. B.

    Published 2006
    “…The result of this project are expected to be a comparison of the used methods that will give an indication how well evolutionary programming can perform relative to conventional method and how good the results of the data mining process.…”
    Get full text
    Get full text
    Conference or Workshop Item
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

    Data mining of protein sequences with amino acid position-based feature encoding technique by Iqbal, M.J., Faye, I., Md Said, A., Samir, B.B.

    Published 2014
    “…In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 classification accuracy over the Yeast protein sequence dataset. …”
    Get full text
    Get full text
    Article
  11. 11

    Seed disperser ant algorithm for optimization / Chang Wen Liang by Chang , Wen Liang

    Published 2018
    “…The classical benchmark problems and composite benchmark functions from Congress on Evolutionary Computation (CEC) 2005 special session is used for validate SDAA. …”
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12
  13. 13
  14. 14

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
    Get full text
    Get full text
    Get full text
    Thesis
  15. 15