In this work,we are interested in ensemble methods by binarization techniques; inparticular,
we focus on the well-know none-vs-one and one-vs-all decomposition strategies, paying special attention
to the final step of the ensembles,the combination of the out puts of the binary classifiers.
Our aim is to develop an empirical analysis of different aggregations to combine these outputs.
To do so,we develop a double study: first,we use different base classifiers in order to observe the suitability and potential of each combination with in each classifier.
Then, we compare the performance of these ensemble techniques with the classifiers’ themselves.
Hence,we also analyse the improvement with respect to the classifiers that handle multiple classes in herently.
We carryouttheexperimentalstudywithseveralwell-knownalgorithmsoftheliteraturesuchas
Support VectorMachines,DecisionTrees,InstanceBasedLearningorRuleBasedSystems.Wewillshow,
supportedbyseveralstatisticalanalyses,thegoodnessofthebinarizationtechniqueswithrespecttothe
baseclassifiersandfinallywewillpointoutthemostrobusttechniqueswithinthisframework.
In this work,we are interested in ensemble methods by binarization techniques; inparticular,we focus on the well-know none-vs-one and one-vs-all decomposition strategies, paying special attentionto the final step of the ensembles,the combination of the out puts of the binary classifiers.Our aim is to develop an empirical analysis of different aggregations to combine these outputs.To do so,we develop a double study: first,we use different base classifiers in order to observe the suitability and potential of each combination with in each classifier.Then, we compare the performance of these ensemble techniques with the classifiers’ themselves.Hence,we also analyse the improvement with respect to the classifiers that handle multiple classes in herently.We carryouttheexperimentalstudywithseveralwell-knownalgorithmsoftheliteraturesuchasSupport VectorMachines,DecisionTrees,InstanceBasedLearningorRuleBasedSystems.Wewillshow,supportedbyseveralstatisticalanalyses,thegoodnessofthebinarizationtechniqueswithrespecttothebaseclassifiersandfinallywewillpointoutthemostrobusttechniqueswithinthisframework.
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