@article{500,
  author = {Andre Bevilaqua, Fabricio Alves Rodrigues, Laurence Rodrigues do Amaral},
  title = {GASNP Classifier: A Machine Learning Environment for Building High-level Biological Knowledge},
  journal = {Journal of Intelligent Computing},
  year = {2011},
  volume = {2},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jic/fulltext/v2n1/1.pdf},
  abstract = {Computational approaches can be applied to solve different biology challenges. Tools based on traditional computation methods have shown, however, to be limited to approach complex biological problems in many situations. In the present study, a machine learning environment (GASNP), based on Genetic Algorithms, is proposed as a tool to extract classification rules from biological dataset. The main goal of the proposed approach is to allow the discovery of concise, and accurate, biological high-level rules which can be used as a classification system. More than focusing only on the classification accuracy, the proposed GASNP model aims at balancing prediction precision, comprehensibility and interpretability. The obtained results show that the suggested approach has great potential and is capable of extracting useful high-level knowledge that could not be extracted by traditional classification methods such as Decision Trees, One R, Single Conjunctive Rule Learner, BFTree, Decision Table, JRIP, PART, among others, using the same dataset.},
}