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

民俗學(xué)

書目名稱Machine Learning, Optimization, and Data Science影響因子(影響力)




書目名稱Machine Learning, Optimization, and Data Science影響因子(影響力)學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science網(wǎng)絡(luò)公開度




書目名稱Machine Learning, Optimization, and Data Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science被引頻次




書目名稱Machine Learning, Optimization, and Data Science被引頻次學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science年度引用




書目名稱Machine Learning, Optimization, and Data Science年度引用學(xué)科排名




書目名稱Machine Learning, Optimization, and Data Science讀者反饋




書目名稱Machine Learning, Optimization, and Data Science讀者反饋學(xué)科排名

Archipelago

生銹

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.

辯論

共同時(shí)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.

滔滔不絕的人

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