thyroid-ann

OpenML dataset with id 40497

No author found.

Full work available at URL: https://api.openml.org/data/v1/download/4535758/thyroid-ann.arff

Upload date: 29 July 2016

Dataset Characteristics

Number of classes: 3
Number of features: 22 (numeric: 21, symbolic: 1 and in total binary: 0 )
Number of instances: 3,772
Number of instances with missing values: 0
Number of missing values: 0

Description

This directory contains Thyroid datasets. "ann-train.data" contains 3772 learning examples and "ann-test.data" contains 3428 testing examples. I have obtained this data from Daimler-Benz. This are the informations I have got together with the dataset:

1. Data setp summary

Number of attributes: 21 (15 attributes are binary,

     6 attributes are continuous)

Number of classes: 3 Number of learning examples: 3772 Number of testing examples: 3428 Data set is availbale on ASCII-file

2. Description

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 datas 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 one Ross Quinlan placed in "pub/machine-learning-databases/thyroid-disease" on "ics.uci.edu". I 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:

"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-Ribiere + 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)

"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.

Randolf Werner (evol@infko.uni-koblenz.de)

This page was built for dataset: thyroid-ann