Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational

MOTTO

Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers,characteristic curve (AUC) and false negative rate (FNR) of proposed algorithms are compared with correlation-based feature selection (FCBF and CFS) and causal based feature selection algorithms (PC, TPDA, GS, IAMB).

aggravate

A Novel Ensemble Technique for Protein Subcellular Location Prediction,rature to solve this problem all the existing approaches are affected by some limitations, so that the problem is still open. Experimental results clearly indicate that the proposed technique, called ., performs equally, if not better, than state of the art ensemble methods aimed at multi-class classification of highly unbalanced data.

易怒

Dedication

Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests,ection to maximize the amount of validation data considering, in turn, each fold as a validation fold to select the trees from. The aim is to increase performance by reducing the variance of the tree ensemble selection process. We demonstrate the effectiveness of our approach on several UCI and real-world data sets.

Hiatal-Hernia

Embedding Random Projections in Regularized Gradient Boosting Machines,ure Random Projections, normalized and uniform binary. Furthermore, we study the effect to keep or change the dimensionality of the data space. Experimental results performed on synthetic and UCI datasets show that Boosting methods with embedded random data projections are competitive to AdaBoost and Regularized Boosting.

FLUSH

The Science of Construction Materialso understand the overall trends when the parameters of the base classifiers – nodes and epochs for NNs –, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.

大喘氣

https://doi.org/10.1007/978-3-642-56257-0me prevents them from scaling up to real-world applications.We propose two methods which enhance correlation-based feature selection such that the stability of feature selection comes with little or even no extra runtime.We show the efficiency of the algorithms analytically and empirically on a wide range of datasets.

FAZE

Osteoarthritis

https://doi.org/10.5822/978-1-61091-205-1strategy divides the training set based on a selected feature and trains a separate classifier for each subset. Experiments are carried out on simulated and real datasets. We report improvement in the final classification accuracy as a result of combining the three strategies.

Bureaucracy

Marco Bertoldi,Paolo Sequi,Tiziano Papi public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.