NOTE: all files associated to this .names file have the ann- prefix.

1. Title: Thyroid Domain

2. Sources: 

   (a) Donors: Randolf Werner
               evol@uniko.uni-koblenz.de
   (b) Obtained from Daimler-Benz.
   (c) Date: October 1992

3. Past Usage:

   (a) "Optimization of the Backpropagation Algorithm for Training Multilayer
       Perceptrons":

       ftp archive.cis.ohio-state.edu  or  ftp 128.146.8.52
       cd pub/neuroprose
       binary
       get schiff.bp_speedup.ps.Z
       quit

       The report is an overview of many different backprop speedup techniques.
       15 different algorithms are described in detail and compared by using
       a big, very hard to solve, practical data set. Learning speed and network
       classification performance with respect to the training data set and also
       with respect to a testing data set are discussed.
       These are the tested algorithms:

       backprop
       backprop (batch mode)
       backprop + Learning rate calculated by Eaton and Oliver's formula
       backprop + decreasing learning rate (Darken and Moody)
       backprop + Learning rate adaptation for each training pattern (J. Schmidhuber)
       backprop + evolutionarily learning rate adaptation (R. Salomon)
       backprop + angle driven learning rate adaptation(Chan and Fallside)
       Polak-R
ibiere + line search (Kramer and Vincentelli)
       Conj. gradient + line search (Leonard and Kramer)
       backprop + learning rate adaptation by sign changes (Silva and Almeida)
       SuperSAB (T. Tollenaere)
       Delta-Bar-Delta (Jacobs)
       RPROP (Riedmiller and Braun)
       Quickprop (Fahlman)
       Cascade correlation (Fahlman)

   (b) "Synthesis and Performance Analysis of Multilayer eural Network Architectures":

       ftp archive.cis.ohio-state.edu  or  ftp 128.146.8.52
       cd pub/neuroprose
       binary
       get schiff.gann.ps.Z
       quit

       In this paper we present various approaches for automatic topology-optimization
       of backpropagation networks. First of all, we review the basics of genetic
       algorithms which are our essential tool for a topology search. Then we give a
       survey of backprop and the topological properties of feedforward networks. We
       report on pioneer work in the filed of topology--optimization. Our first
       approach was based on evolutions strategies which used only mutation to change
       the parent's topologies. Now, we found a way to extend this approach by an
       crossover operator which is essential to all genetic search methods.
       In contrast to competing approaches it allows that two parent networks with
       different number of units can mate and produce a (valid) child network, which
       inherits genes from both of the parents. We applied our genetic algorithm to a
       medical classification problem which is extremly difficult to solve. The
       performance with respect to the training set and a test set of pattern samples
       was compared to fixed network topologies. Our results confirm that the topology
       optimization makes sense, because the generated networks outperform the fixed
       topologies and reach classification performances near optimum.

4. Relevant Information:

   The problem is to determine whether a patient referred to the clinic is
   hypothyroid. Therefore three classes are built: normal (not hypothyroid),
   hyperfunction and subnormal functioning. Because 92 percent of the patients
   are not hyperthyroid a good classifier must be significant better than 92%.

   Note

   These are the attributes Quinlans used in the case study of his article
   "Simplifying Decision Trees" (International Journal of Man-Machine Studies 
   (1987) 221-234).  Unfortunately this data differ from the version already
   present (donated by Ross Quinlan)  I (Randolf Werner) don't know any more 
   details about the dataset. But it's hard to train Backpropagation ANNs with 
   it. The dataset is used in two technical reports (see above).

5. Number of Instances: ann-train.data: 3772, ann-test.data: 3428

6. Number of Classes: 3

7. Number of Attributes: 21 (15 attributes are binary,
			     6 attributes are continuous)