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We introduce a powerful technique to make classifiers more reliable and versatile. Background Check equips classifiers with the ability to assess the difference of unlabelled test data from the training data. In particular, Background Check gives classifiers the capability to (i) perform cautious classification with a reject option, (ii) identify outliers,(More)
Breast cancer is one of the leading causes of death in women. Because of this, thermographic images have received a refocus for diagnosing this cancer type. This work proposes an innovative approach to classify breast abnormalities (malignant, benignant and cyst), employing interval temperature data in order to detect breast cancer. The learning step takes(More)
Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data,(More)
This paper presents learning vector quantization classifiers with adaptive distances. The classifiers furnish discriminant class regions from the input data set that are represented by prototypes. In order to compare prototypes and patterns, the classifiers use adaptive distances that change at each iteration and are different from one class to another or(More)
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