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diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names new file mode 100644 index 00000000..636299d6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/SPECT.names @@ -0,0 +1,89 @@ +1. Title of Database: SPECT heart data + +2. Sources: + -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan + University of Colorado at Denver, Denver, CO 80217, U.S.A. + Krys.Cios@cudenver.edu + Lucy S. Goodenday + Medical College of Ohio, OH, U.S.A. + + -- Donors: Lukasz A.Kurgan, Krzysztof J. Cios + -- Date: 10/01/01 + +3. Past Usage: + 1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S. + "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis" + Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001 + + Results: The CLIP3 machine learning algorithm achieved 84.0% accuracy + References: + Cios, K.J., Wedding, D.K. & Liu, N. + CLIP3: cover learning using integer programming. + Kybernetes, 26:4-5, pp 513-536, 1997 + + Cios, K.J. & Kurgan, L. + Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms, + In: Jain, L.C., and Kacprzyk, J. (Eds.) + New Learning Paradigms in Soft Computing, + Physica-Verlag (Springer), 2001 + + SPECT is a good data set for testing ML algorithms; it has 267 instances + that are descibed by 23 binary attributes + + Other results (in press): + -- CLIP4 algorithm achieved 86.1% accuracy + -- ensemble of CLIP4 classifiers achieved 90.4% accuracy + -- Predicted attribute: OVERALL_DIAGNOSIS (binary) + +4. Relevant Information: + The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images. + Each of the patients is classified into two categories: normal and abnormal. + The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images. + As a result, 44 continuous feature pattern was created for each patient. + The pattern was further processed to obtain 22 binary feature patterns. + The CLIP3 algorithm was used to generate classification rules from these patterns. + The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardilogists' diagnoses). + +5. Number of Instances: 267 +6. Number of Attributes: 23 (22 binary + 1 binary class) +7. Attribute Information: + 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) + 2. F1: 0,1 (the partial diagnosis 1, binary) + 3. F2: 0,1 (the partial diagnosis 2, binary) + 4. F3: 0,1 (the partial diagnosis 3, binary) + 5. F4: 0,1 (the partial diagnosis 4, binary) + 6. F5: 0,1 (the partial diagnosis 5, binary) + 7. F6: 0,1 (the partial diagnosis 6, binary) + 8. F7: 0,1 (the partial diagnosis 7, binary) + 9. F8: 0,1 (the partial diagnosis 8, binary) + 10. F9: 0,1 (the partial diagnosis 9, binary) + 11. F10: 0,1 (the partial diagnosis 10, binary) + 12. F11: 0,1 (the partial diagnosis 11, binary) + 13. F12: 0,1 (the partial diagnosis 12, binary) + 14. F13: 0,1 (the partial diagnosis 13, binary) + 15. F14: 0,1 (the partial diagnosis 14, binary) + 16. F15: 0,1 (the partial diagnosis 15, binary) + 17. F16: 0,1 (the partial diagnosis 16, binary) + 18. F17: 0,1 (the partial diagnosis 17, binary) + 19. F18: 0,1 (the partial diagnosis 18, binary) + 20. F19: 0,1 (the partial diagnosis 19, binary) + 21. F20: 0,1 (the partial diagnosis 20, binary) + 22. F21: 0,1 (the partial diagnosis 21, binary) + 23. F22: 0,1 (the partial diagnosis 22, binary) + -- dataset is divided into: + -- training data ("SPECT.train" 80 instances) + -- testing data ("SPECT.test" 187 instances) +8. Missing Attribute Values: None +9. Class Distribution: + -- entire data + Class # examples + 0 55 + 1 212 + -- training dataset + Class # examples + 0 40 + 1 40 + -- testing dataset + Class # examples + 0 15 + 1 172 |