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Name: Leonardo Villani
Type: User
Name: Leonardo Villani
Type: User
Repositório de trabalhos em grupo da turma 37SCJ da pós na FIAP.
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Repositório para a disciplina de Cloud Development do curso de MBA da FIAP.
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Evaluates the performance of the medical images classification performed by algorithms Binary Relevance and Multi-Label kNN
Evaluates the performance of the medical images classification performed by algorithms Binary Relevance and Multi-Label kNN. The examples of the dataset were characterized with EHD.
Evaluates the performance of the medical images classification performed by algorithms Binary Relevance and Multi-Label kNN. The examples of the dataset were characterized with LBP.
Subset are constructed from a set with more than 12.000 ray-X medical images from breast region to can processed in low-performance computer. The EHD, SIFT, LBP, Gabor and Zernike techniques are used to feature the samples from formed set. The created sets are used to train and test the created model by multi-label classifiers. Finally, are evaluated the performance from selected algorithms to the task of classify images with multiples labels.
Sub-bases were constructed from the bases created in Experiments04. For each ARFF base from Experiments04, more four sub-bases were created, one for each axe of the IRMA code. An evaluating is performed about what extracting features techniques provides features more relevant and for what axis. The performance from the multi-label classification was evaluated too through from problem transform and algorithm adapting approach.
Each ARFF base from Experiments04 are used for train and the nine remaining bases for test in the annotation medical images task. The performance from various classifiers are evaluated to this task. The classifiers used are MLkNN, BRkNN, ClassifierChain(kNN), HMC(kNN) and LabelPowerset(kNN). Furthermore, an evaluating is realized about what extraction features techniques provides more relevant features to the classifiers from this task.
Each ARFF base from Experiments04 are used for train and the nine remaining bases for test in the annotation medical images task. The difference those experiment to the preview experiment is that on Experiments07 the classification is realized by axis instead of assign the labels to all the axes in a only step like on Experiments06. The performance from various classifiers are evaluated to this task. The classifiers used are MLkNN, BRkNN, ClassifierChain(kNN), HMC(kNN) and LabelPowerset(kNN). Furthermore, an evaluating is realized about what extraction features techniques provides more relevant features to the classifiers from this task.
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