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AssigneeTata Consultancy Services Limited
InventorsSri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
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The disclosure presents herein a method to train a classifier in a machine learning using more than one simultaneous sample to address class imbalance problem in any discriminative classifier. A modified representation of the training dataset is obtained by simultaneously considering features based representations of more than one sample. A modification to an architecture of a classifier is needed into handling the modified date representation of the more than one samples. The modification of the classifier directs same number of units in the input layer as to accept the plurality of simultaneous samples in the training dataset. The output layer will consist of units equal to twice the considered number of classes in the classification task, therefore, the output layer herein will have four units for two-class classification task. The disclosure herein can be implemented to resolve the problem of learning from low resourced data.
A computer implemented method to train a machine learning classifier using a plurality of samples in a training dataset, wherein the method comprising one or more steps of:
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