Abstract: An embodiment includes extracting a capability from a dataset representative of a project description of a proposed project using a first machine learning process to form a cluster representative of the capability. The embodiment assigns the capability to a first node of a business operations graph based on a classification result of the capability by a second machine learning process. The embodiment generates a visual indicator based, at least in part, on the assigning of the capability to the first node. The embodiment generates the visual indicator by a process comprising generating a first visual indicator of the capability being assigned to the first node, and a second visual indicator of a development sequence for the capability relative to another capability from the project description based at least in part on an association from the business operations graph between the first node and a second node of the graph.

Abstract: In an approach to creating models utilizing optimally clustered training sets, one or more computer processors determine an optimal cluster size. The one or more computer processors generate one or more clusters from one or more classes and respectively associated training statements that are contained in a training set, based on the determined optimal cluster size, wherein the one or more generated clusters, respectively, contain fewer classes than the training set. The one or more computer processors identify one or more isolated high confidence classes and associated training statements from one or more cluster classifications generated by a static model trained with the one or more generated clusters. The one or more computer processors create one or more dynamic models trained with the one or more identified isolated high confidence classes. The one or more computer processors perform one or more classifications utilizing the one or more created dynamic models.

Abstract: In an approach to improving the predictive accuracy of classifiers, one or more computer processors calculate one or more training set statistics. The one or more computer processors generate one or more balanced training sets based on one or more calculated training set statistics. The one or more computer processors train one or more cognitive models utilizing one or more unbalanced training sets and one or more generated balanced training sets. The one or more computer processors determine a fitness of the one or more trained cognitive models. The one or more computer processors adjust one or more training sets based on the determined fitness of the one or more cognitive models.

Abstract: An embodiment includes extracting a capability from a dataset representative of a project description of a proposed project using a first machine learning process to form a cluster representative of the capability. The embodiment assigns the capability to a first node of a business operations graph based on a classification result of the capability by a second machine learning process. The embodiment generates a visual indicator based, at least in part, on the assigning of the capability to the first node. The embodiment generates the visual indicator by a process comprising generating a first visual indicator of the capability being assigned to the first node, and a second visual indicator of a development sequence for the capability relative to another capability from the project description based at least in part on an association from the business operations graph between the first node and a second node of the graph.

Abstract: Classifying unlabeled input data is provided. Euclidean distance and cosine similarity are calculated between an unlabeled input data point to be classified and a class label centroid of each class within a set of training data. A confidence value is calculated for each class label centroid based on the Euclidean distance and the cosine similarity between the unlabeled input data point and the class label centroid of each class. A highest confidence value equals a best matching class label centroid to the unlabeled input data point. A class label centroid having the highest confidence value is selected. The computer classifies the unlabeled input data point using a class label corresponding to the class label centroid having the highest confidence value.

Abstract: In an approach to creating models utilizing optimally clustered training sets, one or more computer processors determine an optimal cluster size. The one or more computer processors generate one or more clusters from one or more classes and respectively associated training statements that are contained in a training set, based on the determined optimal cluster size, wherein the one or more generated clusters, respectively, contain fewer classes than the training set. The one or more computer processors identify one or more isolated high confidence classes and associated training statements from one or more cluster classifications generated by a static model trained with the one or more generated clusters. The one or more computer processors create one or more dynamic models trained with the one or more identified isolated high confidence classes. The one or more computer processors perform one or more classifications utilizing the one or more created dynamic models.

Abstract: In an approach to improving the predictive accuracy of classifiers, one or more computer processors calculate one or more training set statistics. The one or more computer processors generate one or more balanced training sets based on one or more calculated training set statistics. The one or more computer processors train one or more cognitive models utilizing one or more unbalanced training sets and one or more generated balanced training sets. The one or more computer processors determine a fitness of the one or more trained cognitive models. The one or more computer processors adjust one or more training sets based on the determined fitness of the one or more cognitive models.