✦ LIBER ✦

New probabilistic version of the simca and classy classification methods : Part 2. Practical evaluation

✍ Scribed by Hilko van der Voet; Pierrme M.J. Coenegracht; Jan B. Hemel

Publisher: Elsevier Science
Year: 1986
Tongue: English
Weight: 718 KB
Volume: 191
Category: Article
ISSN: 0003-2670
DOI: 10.1016/s0003-2670(00)86297-2

No coin nor oath required. For personal study only.

✦ Synopsis

The new probabilistic versions of SIMCA and CLASSY described in Part 1 are evaluated. Their classification performance is found to be generally better than those of the old versions. The results are also compared with those of the ALLOC and SLDA classification methods. General over-confident behaviour of the new SIMCA and CLASSY methods as well as ALLOC and SLDA is noted for two of the three data sets investigated (Iris and two wine data sets). DATA AND EVALUATION METHODS *For Part 1 see ref. 1. aPresent address: Agricultural Statistics Department, TN0 Institute of Applied Computer Science (iTi-TNO),

📜 SIMILAR VOLUMES

The improvement of SIMCA classification

The improvement of SIMCA classification by using kernel density estimation : Part 2. Practical evaluation of SIMCA, ALLOC and CLASSY on three data sets

✍ Hilko vander Voet; Durk A. Doornbos 📂 Article 📅 1984 🏛 Elsevier Science 🌐 English ⚖ 664 KB

The performance of the new probabilistic classification method CLASSY is evaluated on three different data sets, together with its predecessors SIMCA and ALLOC. The improvement made over ALLOC is only marginal, whereas CLASSY shows better predictive ability and greater reliability than SIMCA in most

The improvement of SIMCA classification

The improvement of SIMCA classification by using kernel density estimation : Part 1. A new probabilistic classification technique and how to evaluate such a technique

✍ Hilko van der Voet; Durk A. Doornbos 📂 Article 📅 1984 🏛 Elsevier Science 🌐 English ⚖ 661 KB

One of the disadvantages of SIMCA pattern recognition is its inability to produce probabilistic classifications. Attempts to correct this involve distributional assumptions. It appears that SIMCA can handle the residual error terms efficiently, but that inside the class model subspace a crude trunca

An evaluation of SIMCA. Part 2 — classif

An evaluation of SIMCA. Part 2 — classification of pyrolysis mass spectra of pseudomonas and serratia bacteria by pattern recognition using the SIMCA classifier

✍ J. B. M. Dröge; W. J. Rinsma; H. A. Van 'T Klooster; A. C. Tas; J. Van Der Greef 📂 Article 📅 1987 🏛 John Wiley and Sons 🌐 English ⚖ 579 KB

As part of a critical evaluation of the pattern recognition method SIMCA, three data sets containing pyrolysis mass spectra from bacteria were analysed using the SIMCA classifier. Each set consisted of two classes, Pseudomonas and Serratia bacteria, each class containing ten mass spectra and each ma