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This work focuses on the issue of diseases diagnosis based on data classification approaches. We consider mainly the diagnosis of heart diseases, diabetes, hepatitis and fetal risks. To do so, we employ a modified version of the SVDD algorithm, endowed with efficient tools to manage the multi-classification problems. Some other conventional algorithms such as SVM and RBF are, likewise, used to take...
This paper presents a novel combination of filter features selection algorithms for classification problem. Feature selection is one of the most important issues in pattern recognition, machine learning and computer vision. The main objective of feature selection regards the dimensionality reduction, the performance of machine learning improvement and the process comprehensibility increase. Exhaustive...
Supervised learning based classification depends on learning from previously known data set. Here, these data sets governs training for classification of new data points. This training is mainly driven by two fundamental approaches. First one is derivative based approach and another centers around heuristics or direct search based methodologies. Both approaches have their pros and cons depending upon...
In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored. This will indirectly affect how human visualise the data. Therefore, special care is needed to take care of the learning algorithm in order to enhance the accuracy for the minority classes. In this study, the use...
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