The role of biomedical dataset in classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5651 LNAI, Issue , 2009, Pages 370-374

  Tanwani, Ajay Kumar ^a   Farooq, Muddassar ^a  

^a NATIONAL UNIVERSITY OF COMPUTER AND EMERGING SCIENCES   (Pakistan)

Author keywords

Biomedical Datasets; Classification; Complexity Measures

Indexed keywords

BIOMEDICAL DATASETS; CLASSIFICATION; CLASSIFICATION ACCURACY; COMPLEXITY MEASURES; DATA SETS; EMPIRICAL EQUATIONS; FEATURE SELECTION; INFORMATION GAIN; KNOWLEDGE EXTRACTION; MACHINE LEARNING ALGORITHMS; META MODEL; MISSING VALUES; NAIVE BAYES; RULE BASED;

ARTIFICIAL INTELLIGENCE; DECISION TREES; LEARNING SYSTEMS;

LEARNING ALGORITHMS;

EID: 70350220876     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02976-9_51     Document Type: Conference Paper

References (5)

1. Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 289-300 (2002)
2. Tanwani, A.K., Afridi, J., Shafiq, M.Z., Farooq, M.: Guidelines to select machine learning scheme for classifcation of biomedical datasets. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EVOBIO 2009. LNCS, vol. 5483, pp. 128-139. Springer, Heidelberg (2009)
3. UCI repository of machine learning databases, University of California-Irvine, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html
4. Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
5. Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. Journal of Artificial Intelligence Research 11, 131-167 (1999)