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Echocardiogram 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

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Marc Sebban and Richard Nock and Stéphane Lallich. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. Journal of Machine Learning Research, 3. 2002.

a weighted decision rule provides better results than the unweighted rule. Among them, 7 datasets (Balance, Echocardiogram German, Horse Colic, Led, Pima and Vehicle) see important improvements, ranging from 1% to } 5%. In contrast, only one dataset sees significant accuracy decrease (Car,

Gabor Melli. A Lazy Model-Based Approach to On-Line Classification. University of British Columbia. 1989.

The five selected datasets were: echocardiogram hayes-roth, heart, horse-colic,andiris datasets. These datasets (marked in Table 7.1 with a * symbol beside their name) contain a sampling of attribute types and domains. For

D. Randall Wilson and Roel Martinez. Improved Center Point Selection for Probabilistic Neural Networks. Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms.

it retained almost two-thirds of the instances while suffering a large drop in accuracy compared to the other two models. However, in the Echocardiogram dataset, the RPNN used only 9% of the data while improving generalization accuracy by over 12%. Future research will focus on identifying characteristics of applications that help determine whether the RPNN

Zhi-Hua Zhou and Xu-Ying Liu. Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem.

can be used after the IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 12 TABLE XIII AVERAGE Qav VALUES OF THE LEARNERS GENERATED BY OVER-SAMPLING, UNDER-SAMPLING, AND THRESHOLD-MOVING. Two-class data set Qav Qav echocardiogram .779 ± .135 Multi-class data set Cost (a) Cost (b) Cost (c) hepatitis .552 ± .201 lymphography .365 ± .092 .375 ± .170 .308 ± .142 heart s .774 ± .083 glass .615 ± .108 .615 ±

Federico Divina and Elena Marchiori. Handling Continuous Attributes in an Evolutionary Inductive Learner. Department of Computer Science Vrije Universiteit.

deviation between brackets. Tables 4 and 5 contain the results of the experiments on the training and test sets, respectively. On the training sets ECL-LSDf obtains the best performance on all datasets, with optimal performance on the Echocardiogram However, on the test sets, ECL-LSDc achieves the best accuracy in most of the cases, with simplicity (that is, the number of clauses of the output

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