Add to ORKGThis study introduces a new multi-layer multi-component ensemble. The components of this ensemble are trained locally on subsets of features for disjoint sets of data. The data instances are assigned to local regions using the similarity of their features pairwise squared correlation. Many ensemble methods encourage diversity among their base predictors by training them on different subsets of data or different subsets of features. In the proposed architecture the local regions contain disjoint sets of data and for this data only the most similar features are selected. The pairwise squared correlations of the features are used to weight the predictions of the ensemble's models. The proposed architecture has been tested on a number of data s...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Local learning algorithms are plagued with the curse of dimensionality. Locality is introduced based...
This study introduces a new multi-layer multi-component ensemble. The components of this ensemble ar...
AbstractThis study introduces a new multi-layer multi-component ensemble. The components of this ens...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
Abstract — Ensemble learning algorithms train multiple com-ponent learners and then combine their pr...
This work presents a framework for the inclusion of multiple criteria in the design process of super...
Recently, besides the performance, the stability (robust-ness, i.e., the variation in feature select...
In the real world concepts are often not stable but change over time. A typical example of this in t...
[EN]In the machine learning field, especially in classification tasks, the model's design and constr...
In the real world concepts are often not stable but change over time. A typical example of this in t...
In multi-target prediction, an instance has to be classified along multiple target variables at the ...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
Abstract—Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstr...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Local learning algorithms are plagued with the curse of dimensionality. Locality is introduced based...
This study introduces a new multi-layer multi-component ensemble. The components of this ensemble ar...
AbstractThis study introduces a new multi-layer multi-component ensemble. The components of this ens...
A popular technique for modelling data is to construct an ensemble of learners and combine them in t...
Abstract — Ensemble learning algorithms train multiple com-ponent learners and then combine their pr...
This work presents a framework for the inclusion of multiple criteria in the design process of super...
Recently, besides the performance, the stability (robust-ness, i.e., the variation in feature select...
In the real world concepts are often not stable but change over time. A typical example of this in t...
[EN]In the machine learning field, especially in classification tasks, the model's design and constr...
In the real world concepts are often not stable but change over time. A typical example of this in t...
In multi-target prediction, an instance has to be classified along multiple target variables at the ...
In machine learning, ensemble methods combine the predictions of multiple base learners to construct...
Abstract—Ensemble learning strategies, especially Boosting and Bagging decision trees, have demonstr...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Many recent works have shown that ensemble methods yield better generalizability over single classif...
Local learning algorithms are plagued with the curse of dimensionality. Locality is introduced based...