Abstract: The present invention relates to a method for correcting bias introduced by weighted training in machine learning, the method comprises the following steps: S1. label the number of examples of each class in the weighted data used by the machine learning classifier: sum up the total number of training examples Ni from class in the training set, where i can be any of the class labels; S2. calculate the mean weight of examples in each class: in class i of the training data, sum up the weights wij of each example j, then divide by Ni, and label the mean weight wi of this class i; S3. the features of the examples in the weighted data and their corresponding labels are used for classification and regression with a machine learning algorithm; S4. when the classifier calculates probabilities Pw(i) of class i, and correct them by applying the deweighting formula in the deweighter to get accurate probabilities P(i); S5. use the corrected probabilities P(i) to make a classification decision.

Abstract: A method of aggregating and disseminating time sensitive information. A data source is searched to identify recently-posted information items matching predetermined selection criteria. A respective relevance score is calculated for each identified information item. A respective urgency rating is determined for each identified information item. Each information item is triaged using the urgency rating, and disseminated to at least one client based on the triage result.