Arrhythmia Data Set
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Krista Lagus and Esa Alhoniemi and Jeremias Seppa and Antti Honkela and Arno Wagner. INDEPENDENT VARIABLE GROUP ANALYSIS IN LEARNING COMPACT REPRESENTATIONS FOR DATA. Neural Networks Research Centre, Helsinki University of Technology.
models optimized carefully using the IVGA implementation. The model search of our IVGA implementation was able to discover the best grouping, i.e., the one with the smallest cost. 3.2. Arrhythmia data set The identification of different types of heart problems, namely cardiac arrhythmias, is carried out based on electrocardiography measurings from a large number of electrodes. We used a freely
Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. AMultiobjective Genetic Algorithm for Attribute Selection. Computing Laboratory Pontificia Universidade Catolica do Parana University of Kent at Canterbury.
used in the experiments Data Set # examples # attributes # classes Arrhythmia 452 269 16 Dermatology 366 34 6 Vehicle 846 18 4 Promoters 106 57 2 Ionosphere 351 34 2 Crx 690 15 2 All the experiments were performed with a well-known
Shay Cohen and Eytan Ruppin and Gideon Dror. Feature Selection Based on the Shapley Value. School of Computer Sciences Tel-Aviv University.
of 93% with 109 features. For comparison, [Koller and Sahami, 1996] report that the Markov Blanket algorithm yields approximately 600 selected features with accuracy levels of 89% to 93% on this dataset. 1 The Arrhythmia dataset. This dataset is considered to be a difficult one. CSA with backward elimination did best, yielding an accuracy level of 84% with 21 features. Forward selection with higher