✦ LIBER ✦

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

✍ Scribed by Hilko van der Voet; Durk A. Doornbos

Publisher: Elsevier Science
Year: 1984
Tongue: English
Weight: 661 KB
Volume: 161
Category: Article
ISSN: 0003-2670
DOI: 10.1016/s0003-2670(00)85783-9

No coin nor oath required. For personal study only.

✦ Synopsis

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 truncation is used for determining a "normal range", inside which all points are treated as equal. An improvement is made by applying kernel density estimation to the scores inside the class model subspace in combination with a normal error distribution in the remaining dimensions (CLASSY method). The evaluation of these probabilistic classification methods is discussed theoretically.

📜 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