Combining ensemble techniques and re-dimensioning data to increase machine classification accuracy

A computer system for classifying unlabeled input data, the computer system comprising:

• a bus system;
• a storage device connected to the bus system, wherein the storage device stores program instructions;
• and
• a processor connected to the bus system, wherein the processor executes the program instructions to:
• calculate Euclidean distance and cosine similarity between an unlabeled input data point to be classified and a class label centroid of each class within a set of training data;
• calculate a confidence value 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, wherein a highest confidence value equals a best matching class label centroid to the unlabeled input data point;
• select a class label centroid having the highest confidence value;
• and
• classify the unlabeled input data point using a class label corresponding to the class label centroid having the highest confidence value.

A computer program product for classifying unlabeled input data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• calculating, by the computer, Euclidean distance and cosine similarity between an unlabeled input data point to be classified and a class label centroid of each class within a set of training data;
• calculating, by the computer, a confidence value 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, wherein a highest confidence value equals a best matching class label centroid to the unlabeled input data point;
• selecting, by the computer, a class label centroid having the highest confidence value;
• and
• classifying, by the computer, the unlabeled input data point using a class label corresponding to the class label centroid having the highest confidence value.