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SPECT Heart Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info.

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Rich Caruana and Alexandru Niculescu-Mizil. An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI. 2004.

letters as negative, yielding a very unbalanced binary problem. LETTER.p2 uses letters A-M as positives and the rest as negatives, yielding a well balanced problem. HYPER SPECT is the IndianPine92 data set [9] where the difficult class Soybean-mintill is the positive class. SLAC is a problem from the Stanford Linear 1 Department of Computer Science, Cornell University, Ithaca, NY 14853 USA email


Michael G. Madden. Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002.

with the fewest instances, this procedure was repeated 10 times. For the SPECT and Lymphography datasets, the procedure was repeated 50 times to reduce variability. Prediction accuracy results and standard deviations are reported in Table 2. Following usual conventions, for each dataset the algorithm


Lukasz A. Kurgan and Waldemar Swiercz and Krzysztof J. Cios. Semantic Mapping of XML Tags Using Inductive Machine Learning. ICMLA. 2002.

module within the XMapper system. There are several reasons for low performance of the XMapper system without the learning module for the three domains. The spect domain created using the spect dataset [19] had attributes with completely different names between the two sources. Also, all attributes were binary and thus only the relationship between attributes could be used as indicator for mapping


M. A. Galway and Michael G. Madden. DEPARTMENT OF INFORMATION TECHNOLOGY technical report NUIG-IT-011002 Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. Department of Information Technology National University of Ireland, Galway.

with the fewest instances, this procedure was repeated 10 times. For the SPECT and Lymphography datasets, the procedure was repeated 50 times to reduce variability. Prediction accuracy results and standard deviations are reported in Table 2. Following usual conventions, for each dataset the algorithm


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