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Dermatology Data Set

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Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004.

We ended up using only eight of those datasets. We did not use four datasets dermatology soybean, thyroid, audiology) because they have missing attributes, which our current formulation cannot handle. One dataset (ecoli) contains a nominal

Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. Attribute Selection with a Multi-objective Genetic Algorithm. SBIA. 2002.

used in the experiments Data Set # examples # attributes # classes Dermatology 366 36 6 Vehicle 846 18 4 Promoters 106 57 2 Ionosphere 351 34 2 Crx 690 15 2 Arritymia 452 269 16 All the experiments were performed by using a

M. V. Fidelis and Heitor S. Lopes and Alex Alves Freitas. Discovering Comprehensible Classification Rules with a Genetic Algorithm. UEPG, CPD CEFET-PR, CPGEI PUC-PR, PPGIA Praa Santos Andrade, s/n Av. Sete de Setembro.

is fixed, the number of rule conditions (phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public domain, realworld data sets (on the medical domains of dermatology and breast cancer). 1 Introduction This work presents a system based on genetic algorithms (GAs) to perform the task of classification. The system is

Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. PART FOUR: ANT COLONY OPTIMIZATION AND IMMUNE SYSTEMS Chapter X An Ant Colony Algorithm for Classification Rule Discovery. CEFET-PR, Curitiba.

represents a reduction of 20% in the error rate of C4.5. ((96.04 -- 95.02)/(100 -- 95.02) = 0.20) On the other hand, C4.5 discovered rules with a better accuracy rate than AntMiner in the other two data sets. In one data set, Dermatology the difference was quite small, whereas in the Tic-tac-toe the difference was relatively large. (This result will be revisited later.) Overall one can conclude that

Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. An Ant Colony Based System for Data Mining: Applications to Medical Data. CEFET-PR, CPGEI Av. Sete de Setembro, 3165.

AntClass 88.75% 6.73 2.70 0.46 7.50 2.01 C4.5 85.96% 1.07 4.4 0.93 8.5 3.04 Dermatology Data Set AntClass 84.21% 6.34 6.00 0.00 79.00 3.46 C4.5 89.05% 0.62 23.2 1.99 91.7 10.64 Furthermore, Table 3 compares AntMiner with an evolutionary algorithm for rule discovery called ESIA -

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.

into account the standard deviations. The better performance of the GA-found solutions, by comparison with the baseline solution, is particularly significant in the Dermatology and Crx data sets. Table 3. Results comparing multiobjective FSS with conventional FSS Data Set FSS MOFSS solutions Tree size Error rate Total Dominate Dominated Neutral Arrhythmia 31.4 5.35 37.37 1.42 32.2

Perry Moerland. Mixtures of latent variable models for density estimation and classification. E S E A R C H R E P R O R T I D I A P D a l l e M o l l e I n s t i t u t e f o r Pe r cep t ua l A r t i f i c i a l Intelligence .

was used by imposing a small threshold of 0.01 upon the values of R j for Mfas and upon the variance parameters for a diagonal Gmm; this was done for the dermatology NIST, optical, and soybean data sets. 7.2 Experiments: Real-World Data The results of the experiments with Bayes classifiers are listed in Table 6, where the best method and the ones that are not significantly worse (90% on the 5x2cv

H. Altay Guvenir. A Classification Learning Algorithm Robust to Irrelevant Features. Bilkent University, Department of Computer Engineering and Information Science.

of 100 5-fold cross-validation accuracies. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Number of irrelevant features added 0.5 0.6 0.7 0.8 0.9 1.0 Classification accuracy Dermatology data set VFI5 1NN 3NN 5NN 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Number of irrelevant features added 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Classification accuracy Glass data set VFI5 1NN

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