Add to ORKGStudies on drug design datasets are continuing to grow. These datasets are usually known as hard modeled, having a large number of features and a small number of samples. The most common problems in the drug design area are of regression type. Committee machines (ensembles) have become popular in machine learning because of their high performance. In this study, dynamics of ensembles on regression related drug design problems are investigated on a big dataset collection. The study tries to determine the most successful ensemble algorithm, the base algorithm-ensemble pair having the best / worst results, the best successful single algorithm, and the similarities of algorithms according to their performances. We also discuss whether ensembles...
In a wide range of applications, datasets are generated for which the number of variables p exceeds ...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Manag...
Abstract: Drug design datasets are usually known as hard-modeled, having a large number of features ...
In drug discovery, classification is a well established in silico method based on machine learning ...
The application of Machine Learning to cheminformatics is a large and active field of research, but ...
To develop a new ensemble learning method and construct highly predictive regression models in chemo...
Sparse and ensemble methods are the two main approaches in the statistical literature for modeling h...
The motivation of this work is to improve the performance of standard stacking approaches or ensembl...
The use of ensemble models in many problem domains has increased significantly in the last fewyears....
Abstract. The use of ensemble models in many problem domains has increased significantly in the last...
It is well-known that the classification performance of any single classifier is outperformed by a m...
Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machin...
Abstract. The problem of combining predictors to increase accuracy (often called ensemble learning) ...
Recent expansions of technology led to growth and availability of different types of data. This, thu...
In a wide range of applications, datasets are generated for which the number of variables p exceeds ...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Manag...
Abstract: Drug design datasets are usually known as hard-modeled, having a large number of features ...
In drug discovery, classification is a well established in silico method based on machine learning ...
The application of Machine Learning to cheminformatics is a large and active field of research, but ...
To develop a new ensemble learning method and construct highly predictive regression models in chemo...
Sparse and ensemble methods are the two main approaches in the statistical literature for modeling h...
The motivation of this work is to improve the performance of standard stacking approaches or ensembl...
The use of ensemble models in many problem domains has increased significantly in the last fewyears....
Abstract. The use of ensemble models in many problem domains has increased significantly in the last...
It is well-known that the classification performance of any single classifier is outperformed by a m...
Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machin...
Abstract. The problem of combining predictors to increase accuracy (often called ensemble learning) ...
Recent expansions of technology led to growth and availability of different types of data. This, thu...
In a wide range of applications, datasets are generated for which the number of variables p exceeds ...
The main purpose of this study was to determine whether it is possible to somehow use results on tra...
8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Manag...