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thyroid-ann

thyroid-ann

active ARFF Publicly available Visibility: public Uploaded 29-07-2016 by Fisher
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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)

22 features

Class (target)nominal3 unique values
0 missing
V12numeric2 unique values
0 missing
V21numeric324 unique values
0 missing
V20numeric141 unique values
0 missing
V19numeric243 unique values
0 missing
V18numeric72 unique values
0 missing
V17numeric280 unique values
0 missing
V16numeric2 unique values
0 missing
V15numeric2 unique values
0 missing
V14numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V1numeric93 unique values
0 missing
V11numeric2 unique values
0 missing
V10numeric2 unique values
0 missing
V9numeric2 unique values
0 missing
V8numeric2 unique values
0 missing
V7numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V5numeric2 unique values
0 missing
V4numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
Show all 22 features

62 properties

NumberOfInstances
3772
Number of instances (rows) of the dataset.
NumberOfFeatures
22
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
21
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
95.45
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
4.55
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
9.22
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.01
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
2.09
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.03
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
22.53
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.03
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
4.8
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
77.47
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.1
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
8.91
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.2
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.86
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
0.08
Mean of means among attributes of the numeric type.
ClassEntropy
0.45
Entropy of the target attribute values.
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
92.47
Percentage of instances belonging to the most frequent class.
MajorityClassSize
3488
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
3772
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.52
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
61.42
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.46
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
229.97
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
8.32
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.14
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.27
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.2
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
2.47
Percentage of instances belonging to the least frequent class.
MinorityClassSize
93
Number of instances belonging to the least frequent class.
Show all 62 properties

14 tasks

Supervised Classification on thyroid-ann
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on thyroid-ann
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - target_feature: 1
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Clustering on thyroid-ann
0 runs - estimation_procedure: 50 times Clustering
Define a new task