The paper deals with multiclass learning from the perspective of analytically interpreting the results of the analysis as well as that of navigating into them by using interactive visualization tools. It is showed that by combining the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm with the interactive visualization of classification trees provided by the KLIMT software it is possible to highlight important information deriving from the knowledge extraction process without neglecting the prediction accuracy of the classification method. Empirical evidence from two benchmark datasets demonstrates the advantages deriving from the joint use of SASSC and KLIMT
In this paper, we present a novel multiple kernel method to learn the optimal classification functio...
This study proposes a visual approach for classification of multivariate data based on the enhanced ...
Abstract Interactive exploration and analysis of multi-field data utilizes a tight feed-back loop of...
The paper deals with multiclass learning from the perspective of analytically interpreting the resu...
Multiclass Learning (ML) requires a classifier to discriminate instances (objects) among several cla...
This tutorial covers the state-of-the-art research, development, and applications in the KDD area of...
The practice of applying a classifier (called a pattern classifier and abbreviated as PC below) in a...
Classification is a common task in data mining and knowledge discovery. Usually classifiers have to ...
Learning classifier system (LCSs) have the ability to solve many difficult benchmark problems, but t...
The derivation, manipulation and verification of analytical models from raw data is a process which ...
A classification rule is performed to assign a class to new sample indi- viduals. Many times the num...
Methods from supervised machine learning allow the classification of new data automatically and are ...
The increased interest in multimodal data collection in the learning sciences demands for new and po...
We consider the problem of visualizing multidimensional data that has been categorized into classes....
Supervised machine learning techniques require labelled multivariate training datasets. Many approac...
In this paper, we present a novel multiple kernel method to learn the optimal classification functio...
This study proposes a visual approach for classification of multivariate data based on the enhanced ...
Abstract Interactive exploration and analysis of multi-field data utilizes a tight feed-back loop of...
The paper deals with multiclass learning from the perspective of analytically interpreting the resu...
Multiclass Learning (ML) requires a classifier to discriminate instances (objects) among several cla...
This tutorial covers the state-of-the-art research, development, and applications in the KDD area of...
The practice of applying a classifier (called a pattern classifier and abbreviated as PC below) in a...
Classification is a common task in data mining and knowledge discovery. Usually classifiers have to ...
Learning classifier system (LCSs) have the ability to solve many difficult benchmark problems, but t...
The derivation, manipulation and verification of analytical models from raw data is a process which ...
A classification rule is performed to assign a class to new sample indi- viduals. Many times the num...
Methods from supervised machine learning allow the classification of new data automatically and are ...
The increased interest in multimodal data collection in the learning sciences demands for new and po...
We consider the problem of visualizing multidimensional data that has been categorized into classes....
Supervised machine learning techniques require labelled multivariate training datasets. Many approac...
In this paper, we present a novel multiple kernel method to learn the optimal classification functio...
This study proposes a visual approach for classification of multivariate data based on the enhanced ...
Abstract Interactive exploration and analysis of multi-field data utilizes a tight feed-back loop of...