![]() The resulting ensembles are then compared with its components in every one of the classes on the given tasks of vowel identification and writer identification. In this essay, the predictions of classifiers trained on a limited data set are combined by ensemble structures in the search for better classification accuracy. Understanding classifiers’ capabilities and limits in relation to some given data can guide us in the process of either continually looking for better ensemble classifiers or ending the search because no further functionality can be obtained with the base classifiers and data at hand. The comparison can be made over the overall accuracy of the ensemble and its components or it can be made considering not just overall accuracy, but also the accuracy in each class. The measure of improvement can be obtained from comparing the accuracy of the component classifiers with the accuracy of the ensemble. The resulting ensemble can then be evaluated so one can know wheter any improvement happened. ![]() In the context of evolutionary search, trees representing the combination of operations over the outputs of the base classifiers can be looked for and serve as the structure of an ensemble classifier. ![]() This variety of prediction in a set of classifiers can be used to take advantage of the classifiers’ specialties and therefore it can also be used to obtain a better classifier than the previous ones. It is proposed that an ideal ensemble, one which performs at least as good as its best component in every class, can be found with the combination of techniques proposed.Ĭlassifiers created from different techniques and trained in different configurations can also specialise in correctly classifying instances from different subsets of a given data set. ![]() In the present setting of medieval handwriting letter classification and limited data, it is verified how the ensemble classifiers obtained with evolutionary search take advantage of the base classifiers accuracy. Such framework makes use of different techniques like ensemble classifiers, evolutionary search, and convolutional neural networks, besides some preprocessing and data obtention techniques like image flood-filling and image binarization. The aim of this work is to develop and analyse a framework of letter identification in a context of limited data from medieval scripts. Paleographers who study the ageing of old scribes, for instance, need to analyse the features from old documents in order to identify illnesses like the common condition known as “essential tremor”. Besides that, in the context of retrospective diagnosis, documents left by people who passed away are a very important source of information for diagnosis, and sometimes it is the only registry available. Handwriting can give us valuable information about the clinical condition of a person, as handwriting involves the workings of the brain, the eyes, and the hands. The results obtained suggest interesting methods for letter (up to 96% accuracy) and user classification (up to 88% accuracy) in an offline scenario. The best ensembles have their misclassification patterns compared to those of their component classifiers. The misclassification patterns of the base classifiers are analysed in order to determine how much better an ensemble of those classifiers can be than its components. The ensembles are obtained through evolutionary search of trees that aggregate the output of base classifiers, which are neural networks trained prior to the ensemble search. The aim of this research is to use this process as the first step towards classifying the tremor type with more accuracy. ![]() In this work, an investigation is carried about the use of ensembles obtained via evolutionary algorithm for identifying individual letters in tremulous medieval writing and to differentiate between scribes. One way to do this is to search different combinations of classifiers with evolutionary algorithms, which are largely employed when the objective is to find a structure that serves for some purpose. However, to create an ensemble it is necessary to determine how the component classifiers should be combined to generate the final predictions. Ensembles have the ability to use the variety in classification patterns of the smaller classifiers in order to make better predictions. Ensemble classifiers are known for performing good generalization from simpler and less accurate classifiers. ![]()
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