Multivariate Classifi cation for Qualitative Analysis
نویسندگان
چکیده
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Principles of classifi cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 The classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Main categories of classifi cation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Validation and variable selection procedures . . . . . . . . . . . . . . . . . . . . . . . . . 87 Evaluation of classifi cation performances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Classifi cation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Nearest mean classifi er and K-nearest neighbors . . . . . . . . . . . . . . . . . . . . . . 94 Discriminant analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Partial least squares-discriminant analysis (PLS-DA) . . . . . . . . . . . . . . . . . . . 97 Soft independent modeling of class analogy (SIMCA) . . . . . . . . . . . . . . . . . . 97 Artifi cial neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Support vector machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Classifi cation and regression trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 New classifi ers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
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