1. A method comprising: using one or more computers, constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class based on training using a training set of examples comprising positive examples and unlabelled examples with regard to the class; wherein the model comprises a plurality of features, a single hyperparameter, a single threshold parameter, and a decision function; and wherein the training of the model comprises learning a plurality of weighting parameters, each of the plurality of weighting parameters mapping to a feature of the plurality of features, in such a way that positive examples are to be scored higher, in connection with being positive, than unlabelled examples; and wherein the hyperparameter and the threshold parameter are selected for optimal model performance with regard to, in connection with the single class, constraining positive items to be classified as positive while minimizing a number of unlabelled items to be classified as positive; using one or more computers, storing the model, comprising the features, the mapping of the weighting parameters with the features, the weighting parameters, the hyperparameter, the threshold parameter, and the decision function; and using one or more computers, using the decision function for classifying items as positive or negative with regard to the class, wherein the classifying is based at least in part on scores and the threshold parameter.
1. A method comprising: using one or more computers, constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class based on training using a training set of examples comprising positive examples and unlabelled examples with regard to the class; wherein the model comprises a plurality of features, a single hyperparameter, a single threshold parameter, and a decision function; and wherein the training of the model comprises learning a plurality of weighting parameters, each of the plurality of weighting parameters mapping to a feature of the plurality of features, in such a way that positive examples are to be scored higher, in connection with being positive, than unlabelled examples; and wherein the hyperparameter and the threshold parameter are selected for optimal model performance with regard to, in connection with the single class, constraining positive items to be classified as positive while minimizing a number of unlabelled items to be classified as positive; using one or more computers, storing the model, comprising the features, the mapping of the weighting parameters with the features, the weighting parameters, the hyperparameter, the threshold parameter, and the decision function; and using one or more computers, using the decision function for classifying items as positive or negative with regard to the class, wherein the classifying is based at least in part on scores and the threshold parameter. 11. The method of claim 1 , comprising obtaining feature information regarding items, and comprising using the feature information with the model in classifying the items.