By Gjorgji Madjarov, Dejan Gjorgjevikj (auth.), Emilio Corchado, Václav Snášel, Ajith Abraham, Michał Woźniak, Manuel Graña, Sung-Bae Cho (eds.)
The LNAI volumes 7208 and 7209 represent the complaints of the seventh overseas convention on Hybrid man made clever structures, HAIS 2012, held in Salamanca, Spain, in March 2012. The 118 papers released in those complaints have been conscientiously reviewed and chosen from 293 submissions. they're geared up in topical periods on brokers and multi brokers structures, HAIS functions, cluster research, info mining and information discovery, evolutionary computation, studying algorithms, platforms, guy, and cybernetics by means of HAIS workshop, tools of classifier fusion, HAIS for laptop defense (HAISFCS), facts mining: info training and research, hybrid man made intelligence platforms in administration of construction platforms, hybrid synthetic clever structures for ordinal regression, hybrid metaheuristics for combinatorial optimization and modelling advanced structures, hybrid computational intelligence and lattice computing for photograph and sign processing and nonstationary versions of development reputation and classifier combinations.
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Extra resources for Hybrid Artificial Intelligent Systems: 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012. Proceedings, Part II
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Training time of the ML-kNN method is the time needed for calculating the posterior probability of each label within the k nearest neighbors. 5 on all five datasets in terms of the five evaluation measures. The difference in the predictive performances between ML-SVMDT and HOMER is more evident for the larger datasets (bibtex and delicious). Compared to the BR, CC and the ML-kNN methods, ML-SVMDT shows better performance in terms of the ranking based measure for all datasets, except for the bibtex dataset where BR and CC show slightly better results.
The algorithm performs an accuracy-guided forward search to select the most relevant members. Margineantu and Dietterich [11] presented an agreement based ensemble pruning which measures the Kappa statistics between any pair of classifiers. Pairs of classifiers are then selected in ascending order of their agreement level till the desired ensemble size is reached. Rokach et al[12] suggested ranking the classifiers first according to their ROC performance and then evaluating the performance of the ensemble subset by using the top ranked members.
Choose the class with the maximum weighted votes. 8. harmonyensemble(Ω) 9. Begin 10. Initialize the parameters PAR, HMCR,NVAR,NI,BW,HMS 11. Int array X[]; 12. Initialize X[]=0; t=0 13. Fitness=Accuracy of the ensemble 14. For i= 1 to HMS do 15. Generate random binary solutions and append it to HM 16. F[i] = Fitness value of the ith solution vector 17. End for 18. Worstfit = min(F[i]) 19. While (t < NI) 20. For i=1 to NVAR 21. if (rand(0,1) < HMCR) 22. { 23. X[i] = HM[ rand(0, HMS -1), i] a. if (rand(0,1) < PAR) b.