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| Author(s) |
Cebron, N., Berthold, M. |
| Title |
Adaptive fuzzy clustering |
| Abstract |
Classifying large datasets without any a-priori information poses a problem
especially in the field of bioinformatics. In this work, we explore the
task 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. |
| Download |
CeBe06.pdf |
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