textabstractEUR-FEW-CS-97-07 Title Monotone decision trees Author(s) R. Potharst J.C. Bioch T. Petter Abstract In many classification problems the domains of the attributes and the classes are linearly ordered. Often, classification must preserve this ordering: this is called monotone classification. Since the known decision tree methods generate non-monotone trees, these methods are not suitable for monotone classification problems. In this report we provide a number of order-preserving tree-generation algorithms for multi-attribute classification problems with k linearly ordered classes
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
One of the factors hindering the use of classification models in decision making is that their predi...
In many real world applications classification models are required to be in line with domain knowled...
textabstractFor classification problems with ordinal attributes very often the class attribute shoul...
In machine learning, monotone classification is concerned with a classification function to learn in...
textabstractThis paper focuses on the problem of monotone decision trees from the point of view of ...
The objective of data mining is the extraction of knowledge from databases. In practice, one often e...
Monotone constraints are very common while dealing with multi-attribute ordinal problems. Grinding w...
Abstract—In many decision making tasks, values of features and decision are ordinal. Moreover, there...
A two-step procedure for nonparametric rnulticlass classifier design is described. A multiclass recu...
International audienceIn some variants of the supervised classification setting, the domains of the ...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
In many application areas of machine learning, prior knowledge concerning the monotonicity of relati...
In this report we present two new ways of enforcing monotone constraints in regression and classific...
We discuss and test empirically the effects of six dimensions along which existing decision tree ind...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
One of the factors hindering the use of classification models in decision making is that their predi...
In many real world applications classification models are required to be in line with domain knowled...
textabstractFor classification problems with ordinal attributes very often the class attribute shoul...
In machine learning, monotone classification is concerned with a classification function to learn in...
textabstractThis paper focuses on the problem of monotone decision trees from the point of view of ...
The objective of data mining is the extraction of knowledge from databases. In practice, one often e...
Monotone constraints are very common while dealing with multi-attribute ordinal problems. Grinding w...
Abstract—In many decision making tasks, values of features and decision are ordinal. Moreover, there...
A two-step procedure for nonparametric rnulticlass classifier design is described. A multiclass recu...
International audienceIn some variants of the supervised classification setting, the domains of the ...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
In many application areas of machine learning, prior knowledge concerning the monotonicity of relati...
In this report we present two new ways of enforcing monotone constraints in regression and classific...
We discuss and test empirically the effects of six dimensions along which existing decision tree ind...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
One of the factors hindering the use of classification models in decision making is that their predi...
In many real world applications classification models are required to be in line with domain knowled...