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    Hybrid Loss for Improving Classification Performance with Unbalanced Data
    (2020-01-01)
    Lodkaew, Thanawat
    ;
    Pasupa, Kitsuchart
    Unbalanced data is widespread in practice and presents challenges which have been widely studied in classical machine learning. A classification algorithm trained with unbalanced data is likely to be biased towards the majority class and thus show inferior performance on the minority class. To improve the performance of deep neural network (DNN) models on poorly balanced data, we hybridized two well-performing loss functions, specially designed for learning imbalanced data, mean false error and focal loss. Since mean false error can effectively balance between majority and minority classes and focal loss can reduce the contribution of unnecessary samples, which are usually samples from the majority class, which may cause a DNN model to be biased towards the majority class when learning. We show that hybridizing the two losses can improve the classification performance of the model. Our hybrid loss function was tested with unbalanced data sets, extracted from CIFAR-100 and IMDB review datasets, and showed that, overall, it performed better than mean false error or focal loss.
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    An effective classification procedure for diagnosis of prostate cancer in near infrared spectra
    (2010-05-01)
    Kim, Seoung Bum
    ;
    Temiyasathit, Chivalai
    ;
    Bensalah, Karim
    ;
    Tuncel, Altug
    ;
    Cadeddu, Jeffrey
    The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy. © 2009 Elsevier Ltd. All rights reserved.