In multi-label classification, where the evaluation of predictions is less straightforward than in single-label various meaningful, though different, loss functions have been proposed. Ideally, learning algorithm should be customizable towards a specific choice performance measure. Modern implementations boosting, most prominently gradient boosted decision trees, appear to appealing from this p...

Multi-label classifiers allow us to predict the state of a set of responses using a single model. A multi-label model is able to make use of the correlation between the labels to potentially increase the accuracy of its prediction. Critical applications of multi-label classifiers (such as medical diagnoses) require that the system’s confidence in prediction also be provided with the multi-label...

Most recent work has been focused on associative classification technique. Most research work of classification has been done on single label data. But it is not appropriate for some real world application like scene classification, bioinformatics, and text categorization. So that here we proposed multi label classification to solve the issues arise in single label classification. That is very ...