Titlebook: Machine Learning, Optimization, and Data Science; 4th International Co Giuseppe Nicosia,Panos Pardalos,Vincenzo Sciacca Conference proceedi

民俗学

书目名称Machine Learning, Optimization, and Data Science影响因子(影响力)




书目名称Machine Learning, Optimization, and Data Science影响因子(影响力)学科排名




书目名称Machine Learning, Optimization, and Data Science网络公开度




书目名称Machine Learning, Optimization, and Data Science网络公开度学科排名




书目名称Machine Learning, Optimization, and Data Science被引频次




书目名称Machine Learning, Optimization, and Data Science被引频次学科排名




书目名称Machine Learning, Optimization, and Data Science年度引用




书目名称Machine Learning, Optimization, and Data Science年度引用学科排名




书目名称Machine Learning, Optimization, and Data Science读者反馈




书目名称Machine Learning, Optimization, and Data Science读者反馈学科排名

Archipelago

生锈

charisma

Feature Based Multivariate Data Imputation,ent experimental settings: ., . and . with 25% missing data in the test set over five-fold cross validation. Furthermore, the proposed model has straightforward implementation and can easily incorporate other imputation techniques.

Nausea

Information-Theoretic Feature Selection Using High-Order Interactions,erived from information theory. We show that our method is able to find interactions which remain undetected when using standard methods. We prove some theoretical properties of the introduced criterion and interaction information.

辩论

共同时代

Generating Term Weighting Schemes Through Genetic Programming, generates a new TWS based on the performance of the learning method. We experience the generated TWSs on three well-known benchmarks. Our study shows that even early generated formulas are quite competitive with the state-of-the-art TWSs and even in some cases outperform them.

滔滔不绝的人

Adaptive Dimensionality Reduction in Multiobjective Optimization with Multiextremal Criteria,ion accelerating the search is presented. Efficiency of the proposed approach is demonstrated on the base of representative computational experiment on a test class of bi-criterial MCO problems with essentially multiextremal criteria.

instill

Optimization of Neural Network Training with ELM Based on the Iterative Hybridization of Differentiaining/Testing) obtains the best results, followed by DECC-G and MOS. All three algorithms obtain better results than M-ELM. The experimentation was carried out on 38 classification problems recognized by the scientific community, while Friedman and Wilcoxon nonparametric statistical tests support the results.

Ingrained