Datasets used for classification: comparison of results

Before using any new dataset it should be described here!
Results from the Statlog project are here.
Logical rules derived for data are here.

Medical:  Appendicitis | Breast cancer (Wisconsin) | Breast Cancer (Ljubljana) | Diabetes (Pima Indian) | Heart disease (Cleveland) | Heart disease (Statlog version) | Hepatitis | Hypothyroid | Hepatobiliary disorders |

Other datasets:  Ionosphere | Satellite image dataset (Statlog version) | Sonar | Telugu Vovel | Vovel | Wine | Other data: Glass, DNA |
More results for   Statlog datasets.

A note of caution: comparison of different classifiers is not an easy task. Before you get into ranking of methods using the numbers presented in tables below please note the following facts. Here relatively small data are analyzed, and simple classification methods are used, but not all task require large deep learning systems, sometimes simpler is better.

Many results we have collected give only a single number (even results from the StatLog project!), without standard deviation. Since most classifiers may give results that differ by several percent on slightly different data partitions single numbers do not mean much.

Leave-one-out tests have been criticized as a basis for accuracy evaluation, the conclusion is that crossvalidation is safer, cf:
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. of the 14th Int. Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 1137-1143.

Crossvalidation tests (CV) are also not ideal. Theoretically about 2/3 of results should be within a single standard deviation from the average, and 95% of results should be within two standard deviations, so in a 10-fold crossvalidation you should see very rarely reuslts that are beter or worse than 2xSTDs. Running CV several times may also give you different answers. Search for the best estimator continues. Cf:
Dietterich, T. (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10 (7), 1895-1924;
Nadeau C, Bengio Y. (1999) Inference for the Generalization Error. Tech. rep. 99s-25, CIRANO, J. Machine Learning (Kluver, in print).

Even the best accuracy and variance estimation is not sufficient, since performance cannot be characterized by a single number. It should be much better to provide full Receiver Operator Curves (ROC). Combining ROC with variance estimation would be ideal.
Unfortunately this still remains to be done. All we can do now is to collect some numbers in tables.

Our results are obtained usually with the GhostMiner package, developed in our group.
Some publications with results are on my page.

TuneIT, Testing Machine Learning & Data Mining Algorithms - Automated Tests, Repeatable Experiments, Meaningful Results.

Appendicitis.

106 vectors, 8 attributes, two classes (85 acute a. +21 other, or 80.2+19.8%), data from Shalom Weiss;
Results obtained with the leave-one-out test, % of accuracy given
Attribute names: WBC1, MNEP, MNEA, MBAP, MBAA, HNEP, HNEA

For 90% accuracy and p=0.95 confidence level 2-tailed bounds are: [82.8%,94.4%]

S.M. Weiss, I. Kapouleas, "An empirical comparison of pattern recognition, neural nets and machine learning classification methods", in: J.W. Shavlik and T.G. Dietterich, Readings in Machine Learning, Morgan Kauffman Publ, CA 1990
H.J. Hamilton, N. Shan, N. Cercone, RIAC: a rule induction algorithm based on approximate classification, Tech. Rep. CS 96-06, Regina University 1996.

C-MLP2LN  (logical rules) only estimated  l-o-o  since the rules are like PVM.
3 crisp logical rules, overall  91.5% accuracy

Results for 10-fold stratified crossvalidation

Maszczyk T, Duch W, Support Feature Machine, WCCI 2010 (submitted).

Wisconsin breast cancer.

From UCI repository, 699 cases, 9 attributes, two classes, 458 (65.5%) & 241 (34.5%).
Results obtained with the leave-one-out test, % of accuracy given.

F6 has 16 missing values, removing these vectors leaves 683 examples.

H.J. Hamilton, N. Shan, N. Cercone, RIAC: a rule induction algorithm based on approximate classification, Tech. Rep. CS 96-06, Regina University 1996.

Results obtained with the 10-fold crossvalidation, 16 vectors with F6 values missing removed, 683 samples left, % of accuracy given.

Results obtained with the 10-fold crossvalidation, % of accuracy given, all data, missing vlues handled in different ways.

For 97% accuracy and p=0.95 confidence level 2-tailed bounds are: [95.5%,98.0%]

K.P. Bennett, J. Blue, A Support Vector Machine Approach to Decision Trees, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997

N. Shang, L. Breiman, ICONIP'96, p.133

B. Ster and A. Dobnikar, Neural networks in medical diagnosis: Comparison with other methods. In A. Bulsari et al., editor, Proceedings of the International Conference EANN '96, pages 427-430, 1996.

F. Zarndt, A Comprehensive Case Study: An Examination of Machine Learning and Connectionist Algorithms, MSc Thesis, Dept. of Computer Science, Brigham Young University, 1995

Breast Cancer (Ljubljana data)

From UCI repository (restricted):  286 instances, 201 no-recurrence-events (70.3%), 85 recurrence-events (29.7%);
9 attributes, between 2-13 values each, 9 missing values

Results - 10xCV? Sometimes methodology was unclear;
difficult, noisy data, some methods are below the base rate (70.3%).

For 78% accuracy and p=0.95 confidence level 2-tailed bounds are: [72.9%,82.4%]

• Assistant-86 achieved 78 %, but this seems to be best result that happens in some crossvalidations, not the average.
• Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
• Blanchard, G., Schafer,C., Rozenholc,Y., &Muller,K.-R. (2007) Optimal dyadic decision trees. Machine Learning 66: 709-717.
• Clark,P. & Niblett,T. (1987). Induction in Noisy Domains.  In: Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled,  Yugoslavia: Sigma Press.
• Porter R.B., G. Beate Zimmer, Don R. Hush: Stack Filter Classifiers. ISMM 2009: 282-294
• Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains.  In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann.
• Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI.
• F. Zarndt, A Comprehensive Case Study: An Examination of Machine Learning and Connectionist Algorithms, MSc Thesis, Dept. of Computer Science, Brigham Young University, 1995
• S.M. Weiss, I. Kapouleas. An empirical comparison of pattern recognition, neural nets and machine learning classification methods, in: J.W. Shavlik and T.G. Dietterich, Readings in Machine Learning, Morgan Kauffman Publ, CA 1990

Hepatitis.

From UCI repository, 155 vectors, 19 attributes,
Two classes, die with 32 (20.6%), live with 123 (79.4%).
Many missing values! F18 has 67 missing values, F15 has 29, F17 has 16 and other features between 0 and 11.

Results obtained with the leave-one-out test, % of accuracy given

MLP, CART, LDA results from (check it ?) S.M. Weiss, I. Kapouleas, "An empirical comparison of pattern recognition, neural nets and machine learning classification methods", in: J.W. Shavlik and T.G. Dietterich, Readings in Machine Learning, Morgan Kauffman Publ, CA 1990.
Other results - our own;

Results obtained with the 10-fold crossvalidation, % of accuracy given; our results with stratified crossvalidation, other results - who knows? Differences for this dataset are rather small, 0.1-0.2%.

Results on BP, LVQ, ..., SNB are from: B. Ster and A. Dobnikar, Neural networks in medical diagnosis: Comparison with other methods. In A. Bulsari et al., editor, Proceedings of the International Conference EANN '96, pages 427-430, 1996.
Our good results reflect superior handling of missing values ?
Duch W, Grudziński K (1998) A framework for similarity-based methods. Second Polish Conference on Theory and Applications of Artificial Intelligence, Lodz, 28-30 Sept. 1998, pp. 33-60
Weighted kNN: Duch W, Grudzinski K and Diercksen G.H.F (1998) Minimal distance neural methods. World Congress of Computational Intelligence, May 1998, Anchorage, Alaska, IJCNN'98 Proceedings, pp. 1299-1304

Statlog version of Cleveland Heart disease.

13 attributes (extracted from 75), no missing values.
270=150+120 observations selected from the 303 cases (Cleveland Heart).

Attribute Information:
 

Attributes types: Real: 1,4,5,8,10,12;  Ordered:11,   Binary: 2,6,9   Nominal:7,3,13
Classes: Absence (1) or presence (2) of heart disease;

In Statlog experiments on heart data cost or risk matrix has been used with 9-fold crossvalidation, only cost values are given.
Results below are obtained with the 10-fold crossvalidation, % of accuracy given, no risk matrix

Results for Heart and other Statlog datasest are collected here.

Cleveland heart disease.

From UCI repository, 303 cases, 13 attributes (4 cont, 9 nominal), 7 vectors with missing values ?
2 (no, yes) or 5 classes (no, degree 1, 2, 3, 4).
Class distribution: 164 (54.1%) no, 55+36+35+13 yes (45.9%) with disease degree 1-4.

Results obtained with the leave-one-out test, % of accuracy given, 2 classes used.

MLP, CART, LDA where are these results from ???
Other results - our own.

Results obtained with the 10-fold crossvalidation, % of accuracy given.
Ster & Dobnikar reject 6 vectors (leaving 297) with missing values.
We use all 303 vectors replacing missing values by means for their class; in KNN we have used Stalog convention, 297 vectors

For 85% accuracy and p=0.95 confidence level 2-tailed bounds are: [80.5%,88.6%]

Results obtained with BP, LVQ, ..., SNB are from: B. Ster and A. Dobnikar, Neural networks in medical diagnosis: Comparison with other methods. In: A. Bulsari et al., editor, Proceedings of the International Conference EANN '96, pages 427-430, 1996.

Magnus Stensmo and Terrence J. Sejnowski, A Mixture Model System for Medical and Machine Diagnosis, Advances in Neural Information Processing Systems 7 (1995) 1077-1084

Kristin P. Bennett, J. Blue, A Support Vector Machine Approach to Decision Trees, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997

Other results for this dataset (methodology sometimes uncertain):
D. Wettschereck, averaging 25 runs with 70% train and 30% test, variants of k-NN with different metric functions and scaling.

David Aha & Dennis Kibler - From UCI repository past usage

Gennari, J.H., Langley, P, Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11-61.

Friedman N, Geiger D, Goldszmit M (1997). Bayesian networks classifiers. Machine Learning 29: 131--163

Diabetes.

From the UCI repository, dataset "Pima Indian diabetes":
2 classes, 8 attributes, 768 instances, 500 (65.1%) negative (class1), and 268 (34.9%) positive tests for diabetes. class2.
All patients were females at least 21 years old of Pima Indian heritage.

Attributes used:
1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skin fold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)

Results obtained with the 10-fold crossvalidation, % of accuracy given; Statlog results are with 12-fold crossvalidation

For 77.7% accuracy and p=0.95 confidence level 2-tailed bounds are: [74.6%,80.5%]

Results on BP, LVQ, ..., SNB are from: B. Ster and A. Dobnikar, Neural networks in medical diagnosis: Comparison with other methods. In A. Bulsari et al., editor, Proceedings of the International Conference EANN '96, pages 427-430, 1996.

• Bennett K.P, J. Blue, A Support Vector Machine Approach to Decision Trees, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997
• Blanchard, G., Schafer,C., Rozenholc,Y., &Muller,K.-R. (2007) Optimal dyadic decision trees. Machine Learning 66: 709-717.
• Michie D, D.J. Spiegelhalter, C.C. Taylor (eds), Machine Learning, Neural and Statistical Classification, Stalog project book.
• Porter R.B., G. Beate Zimmer, Don R. Hush: Stack Filter Classifiers. ISMM 2009: 282-294
• Shang N, L. Breiman, ICONIP'96, p.133

Other results (with different tests):

Friedman N, Geiger D, Goldszmit M (1997). Bayesian networks classifiers. Machine Learning 29: 131--163

Opper/Winther use 200 training and 332 test examples (following Rippley), with TAP MFT results on test 81%, SVS at 80.1% and best NN as 77.4%.

Hypothyroid.

Thyroid, From UCI repository, dataset "ann-train.data": A Thyroid database suited for training ANNs.
3772 learning and 3428 testing examples; primary hypothyroid, compensated hypothyroid, normal.
Training: 93+191+3488 or 2.47%, 5.06%, 92.47%
Test: 73+177+3178 or 2.13%, 5.16%, 92.71%
21 attributes (15 binary, 6 continuous); 3 classes

The problem is to determine whether a patient referred to the clinic has 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 data Quinlan has used in the case study of in the article "Simplifying Decision Trees" (International Journal of Man-Machine Studies (1987) 221-234)

Names: I (W.D.) have investigated this issue and after some mail exchange with Chris Mertz, who maintains the UCI repository; here is the conclusion:

Results:

For 99.90% accuracy on training and p=0.95 confidence level 2-tailed bounds are: [99.74%,99.96%]

Most NN results from W. Schiffmann, M. Joost, R. Werner, 1993; MLP2LN and Init+a,b ours.
k-NN, PVM and CART from S.M. Weiss, I. Kapouleas, "An empirical comparison of pattern recognition, neural nets and machine learning classification methods", in: J.W. Shavlik and T.G. Dietterich, Readings in Machine Learning, Morgan Kauffman Publ, CA 1990
SVM with linear and Gaussian kernels gives quite poor results on this data.

3 crisp logical rules using TSH, FTI, T3, on_thyroxine, thyroid_surgery, TT4  give 99.3% of accuracy on the test set.

Hepatobiliary disorders

Contains medical records of 536 patients admitted to a university-affiliated Tokyo-based hospital, with four types of hepatobiliary disorders: alcoholic liver damage, primary hepatoma, liver cirrhosis and cholelithiasis. The records included results of 9 biochemical tests and sex of the patient. The same 163 cases as in [Hayashi et.al]  were used as the test data.

FSM gives about 60 Gaussian or triangular membership functions achieving accuracy of 75.5-75.8%. Rotation of these functions (i.e. introducing linear combination of inputs to the rules) does not improve this accuracy. 10-fold crossvalidation tests on the mixed, training plus test data, give similar results. The best results were obtained with the K* method based on algorithmic complexity optimization, giving 78.5% on the test set, and kNN with Manhattan distance function, k=1 and selection of features (using the leave-one-out method on the training data, features 2, 5, 6 and 9 were removed), giving 80.4% accuracy. Simulated annealing optimization  of the scaling factors for the remaining 5 features give 81.0% and optimizing scaling factors using all input features 82.8%. The scaling factors are: 0.92, 0.60, 0.91, 0.92, 0.07, 0.41, 0.55, 0.86, 0.30. Similar accuracy is obtained using multisimplex method for optimization of the scaling factors.

Y. Hayashi, A. Imura, K. Yoshida, Fuzzy neural expert system and its application to medical diagnosis. In: 8th International Congress on Cybernetics and Systems, New York City 1990, pp. 54-61

S. Mitra, R. De, S. Pal, Knowledge based fuzzy MLP for classification and rule generation. IEEE Transactions on Neural Networks 8, 1338-1350, 1997, a knowledge-based fuzzy MLP system gives results on the test set in the range from 33% to 66.3%, depending on the actual fuzzy model used.

W. Duch and K. Grudziński, ``Prototype Based Rules - New Way to Understand the Data,'' Int. Joint Conference on Neural Networks, Washington D.C., pp. 1858-1863, 2001. Contains best results with 1-NN, Canberra and feature selection, 83.4% on the test.

Landsat Satellite image dataset (STATLOG version)

Training 4435 test 2000 cases, 36 semi-continuous [0 to 255] attributes (= 4 spectral bands x 9 pixels in neighborhood) and 6 decision classes: 1,2,3,4,5 and 7 (class 6 has been removed because of doubts about the validity of this class).

The StatLog database consists of the multi-spectral values of pixels in 3x3 neighborhoods in a satellite image, and the classification associated with the central pixel in each neighborhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number.

The original database was generated from Landsat Multi-Spectral Scanner image data. The sample database was generated taking a small section (82 rows and 100 columns) from the original data. One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels.

The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighborhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighborhood and a number indicating the classification label of the central pixel. In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighborhood straddles a boundary.

All results from Statlog book, except GM - GhostMiner calculations, W. Duch.

Machine Learning, Neural and Statistical Classification, D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), Stalog project book!

Ionosphere

351 data records, with class division 224 (63.8%) + 126 (35.9%). Usually first 200 vectors are taken for training, and last 151 for the test, but this is very unbalanced: in the training set 101 (50.5%) and 99 (49.5%) are from 1/2 class, in the test set 123 (82%) and 27 (18%) are from class 1/2.
34 attributes, but f2=0 always and should be removed; f1 is binary, the remaining 32 attributes are continuous.
2 classes - different types of radar signals reflected from ionosphere.

Some vectors: 8, 18, 20, 22, 24, 30, 38, 52, 76, 78, 80, 82, 103, 163, 169, 171, 183, 187, 189, 191, 201, 215, 219, 221, 223, 225, 227, 229, 231, 233, 249, are either binary 0, 1 or have only 3 values -1, 0, +1.
For example, vector 169 has only one component = 1, all others are 0.

Perceptron+MLP results:
Sigillito, V. G., Wing, S. P., Hutton, L. V., & Baker, K. B. (1989)  Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10, 262-266.
N. Shang, L. Breiman, ICONIP'96, p.133
David Aha: k-NN+C4+IB3, from Aha, D. W., & Kibler, D. (1989). Noise-tolerant instance-based learning algorithms. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 794-799). Detroit, MI: Morgan Kaufmann.
IB3 parameter settings: 70% and 80% for acceptance and dropping respectively.
RIAC, C4.5 from: H.J. Hamilton, N. Shan, N. Cercone, RIAC: a rule induction algorithm based on approximate classification, Tech. Rep. CS 96-06, Regina University 1996.
K.P. Bennett, J. Blue, A Support Vector Machine Approach to Decision Trees, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY, 1997

Training/test division is not too good in this case, distributions are a bit different.
In 10xCV results are:

VSS is an MLP with search, implemented by Mirek Kordos, used with 3 epochs; neurons may be sigmoidal or step-wise (64 values).
Maszczyk T, Duch W, Support Feature Machine, WCCI 2010 (submitted).

Sonar: Mines vs Rocks

208 cases, 60 continuous attributes, 2 classes, 111 metal, 97 rock.
From the CMU benchmark repository

This dataset has been used in two kinds of experiments:
1. The "aspect-angle independent" experiments use all 208 cases with 13-fold crossvalidation, averaged over 10 runs to get std.
2. The "angle independent experiments" use training / test sets with 104 vectors each. Class distribution in training is 49 + 55, in test 62 + 42.

Estimation of L1O on the whole dataset (Opper and Winther) give 78.2% only; is the test so easy? Some of this results were made without standardization of the data, which is here very important!

The "angle independent experiments" with training / test sets.

The "angle dependent experiments" with 13 CV on all data.

M. Opper and O. Winther, Gaussian Processes and SVM: Mean Field Results and Leave-One-Out. In: Advances in Large Margin Classifiers, Eds. A. J. Smola, P. Bartlett, B. Sch�lkopf, D. Schuurmans, MIT Press, 311-326, 2000; same methodology as Gorman with Sejnowski.

N. Shang, L. Breiman, ICONIP'96, p.133, 10xCV

Gorman, R. P., and Sejnowski, T. J. (1988).  "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets", Neural Networks 1, pp. 75-89,  13xCV

Our results: kNN results from 10xCV and from 13xCV are quite similar, so Shang and Breiman should not differ much from 13 CV.

WD Leave-one-out (L1O) estimations on std data:
L1O with k=1, Euclidean distance, for all data gives 87.50%, other k and distance function do not give significant improvement.
SVM linear, C=1, L1O 75.0%, for Gaussian kernel, C=1, L1O is 78.8%

Other L1O results taken from C. Domeniconi, J. Peng, D. Gunopulos, "An adaptive metric for pattern classification".

Vovel

528 training, 462 test cases, 10 continuous attributes, 11 classes
From the UCI benchmark repository.

Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios.

Results on the total set

N. Shang, L. Breiman, ICONIP'96, p.133, made 10xCv instead of using the test set.

Telugu Vovel

871 patterns, 6 overlapping vowel classes (Indian Telugu vowel sounds), 3 features (formant frequencies).

Parameters in SVM were optimized, that is in each CV different parameters were used, so only approximate value can be quoted. If they are fixed to C=1000, s=1 results are a bit worse.

Papers using this data:

• S. K. Pal and D. Dutta Majumder, ``Fuzzy sets and decision making approaches in vowel and speaker recognition'', IEEE Transactions on Systems, Man, and Cybernetics, Vol. 7, pp. 625-629, 1977.
• S. Mitra, M. Banerjee and S. K. Pal, Rough knowledge-based network, fuzziness and classification, Neural Computing & Applications 7, 17-25, 1998.
• Duch W and Hayashi Y, Computational intelligence methods and data understanding. In: Quo Vadis computational Intelligence? New trends and approaches in computational intelligence. Eds. P. Sincak, J. Vascak, Springer studies in fuzziness and soft computing, Vol. 54 (2000), pp. 256-270.
• Chaoshun Li, Jianzhong Zhou, Qingqing Li and Xiuqiao Xiang, A Fuzzy Cluster Algorithm Based on Mutative Scale Chaos Optimization, LNCS 5264, 259-267, 2008.

Wine data

Source: UCI, described in Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Class distribution: 178 cases = [59, 71, 48] in Class 1-3;
13 continuous attributes: alcohol, malic-acid, ash, alkalinity, magnesium, phenols, flavanoids, nonanthocyanins, proanthocyanins, color, hue, OD280/D315, proline.

UCI past usage:
[1] S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland (submitted to Technometrics).
[2] S. Aeberhard, D. Coomans and O. de Vel, "The classification performance of RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland (submitted to Journal of Chemometrics).

Other Data

Glass identification

Shang, Breiman CART 71.4% accuracy,  DB-CART 70.6%.

Leave-one-out results taken from C. Domeniconi, J. Peng, D. Gunopulos, "An adaptive metric for pattern classification".

DNA-Primate splice-junction gene sequences, with associated imperfect domain theory.

Stalog Data: splice junctions are points on a DNA sequence at which `superfluous' DNA is removed during the process of protein creation in higher organisms. The problem posed in this dataset is to recognize, given a sequence of DNA, the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out).
This problem consists of two subtasks: recognizing exon/intron boundaries (referred to as EI sites), and recognizing intron/exon boundaries (IE sites). (In the biological community, IE borders are referred to a "acceptors'' while EI borders are referred to as "donors''.)

Number of Instances: 3190. Class distribution:

Number of attributes: originally 60 attributes {a,c,t,g}, usually converted to 180 binary indicator variables {(0,0,0), (0,0,1), (0,1,0), (1,0,0)}, or 240 binary variables.
Much better performance is generally observed if attributes closest to the junction are used (middle). In the StatLog version (180 variables), this means using attributes A61 to A120 only.

kNN GM - GhostMiner version of kNN (our group)
SSV Decision Tree - our results

Links to other Duch-Lab projects, many talks and to other papers on various subjects.

Maintained by Wlodzislaw Duch.