Adaptive prototype-based fuzzy classification

Cebron, Nicolas; Berthold, Michael R.

Adaptive prototype-based fuzzy classification

Dateien

Cebron_opus-117826.pdfGröße: 486.02 KBDownloads: 279

Datum

2008

Autor:innen

Cebron, Nicolas

Berthold, Michael R.

Publikationstyp

Zeitschriftenartikel

unikn.publication.listelement.citation.prefix.version.undefined

Fuzzy Sets and Systems. 2008, 159(21), pp. 2806-2818. Available under: doi: 10.1016/j.fss.2008.03.019

Zusammenfassung

Classifying large datasets without any a priori information poses a problem especially in the field of bioinformatics. In this work, we explore the problem of classifying hundreds of thousands of cell assay images obtained by a high-throughput screening camera. The goal is to label a few selected examples by hand and to automatically label the rest of the images afterwards. Up to now, such images are classified by scripts and classification techniques that are designed to tackle a specific problem. We propose a new adaptive active clustering scheme, based on an initial fuzzy c-means clustering and learning vector quantization. This scheme can initially cluster large datasets unsupervised and then allows for adjustment of the classification by the user. Motivated by the concept of active learning, the learner tries to query the most ''useful'' examples in the learning process and therefore keeps the costs for supervision at a low level. A framework for the classification of cell assay images based on this technique is introduced. We compare our approach to other related techniques in this field based on several datasets.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

active learning, cell assays, classification, fuzzy clustering, image mining, noise handling

Zitieren

ISO 690

CEBRON, Nicolas, Michael R. BERTHOLD, 2008. Adaptive prototype-based fuzzy classification. In: Fuzzy Sets and Systems. 2008, 159(21), pp. 2806-2818. Available under: doi: 10.1016/j.fss.2008.03.019

BibTex

@article{Cebron2008Adapt-2977,
  year={2008},
  doi={10.1016/j.fss.2008.03.019},
  title={Adaptive prototype-based fuzzy classification},
  number={21},
  volume={159},
  journal={Fuzzy Sets and Systems},
  pages={2806--2818},
  author={Cebron, Nicolas and Berthold, Michael R.}
}

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