Multi-polytope machine for classification

A computer program product for improving a computing efficiency of a computing device in generating a classifier engine for machine learning, the computer program product comprising:

• one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more non-transitory computer readable storage media, the program instructions comprising:
• receiving a plurality of data points;
• applying a semi-supervised k-means process to a first set of data points of the plurality of data points and a second set of data points of the plurality of data points, wherein
• the first set of data points is labeled with a first class of a plurality of classes, and
• the second set of data points is labeled with a second class of the plurality of classes, different from the first class;
• clustering the first set of data points and the second set of data points into a first plurality of clusters and a second plurality of clusters, respectively, using the semi-supervised k-means process;
• constructing multi-polytopes for the first plurality of clusters and the second plurality of clusters;
• training, based on the constructing of the multi-polytopes, a plurality of support vector machine (SVM) classifiers, wherein each SVM classifier of the plurality of SVM classifiers is trained on a respective pair of clusters within the first plurality of clusters and the second plurality of clusters;
• determining, using the trained plurality of SVM classifiers, separation hyperplanes for the first plurality of clusters and the second plurality of clusters;
• and
• determining labels for each cluster of the first plurality of clusters and each cluster of the second plurality of clusters, based on the separation hyperplanes.

A computer server for improving computational efficiency in generating a classifier engine for machine learning, the computer server comprising:

• a network connection;
• one or more computer readable storage media;
• a processor coupled to the network connection and coupled to the one or more computer readable storage media;
• and
• a computer program product comprising program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• receiving a plurality of data points;
• applying a semi-supervised k-means process to a first set of data points of the plurality of data points and a second set of data points of the plurality of data points, wherein
• the first set of data points is labeled with a first class of a plurality of classes, and
• the second set of data points is labeled with a second class of the plurality of classes, different from the first class;
• clustering the first set of data points and the second set of data points into a first plurality of clusters and a second plurality of clusters, respectively, using the semi-supervised k-means process;
• constructing multi-polytopes for the first plurality of clusters and the second plurality of clusters;
• training, based on the constructing of the multi-polytopes, a plurality of support vector machine (SVM) classifiers, wherein each SVM classifier of the plurality of SVM classifiers is trained on a respective pair of clusters within the first plurality of clusters and the second plurality of clusters;
• determining, using the trained plurality of SVM classifiers, separation hyperplanes for the first plurality of clusters and the second plurality of clusters;
• and
• determining labels for each cluster of the first plurality of clusters and each cluster of the second plurality of clusters, based on the separation hyperplanes.

A software as a service (SaaS) computer-implemented method for improving a computational efficiency in generating a classifier engine for machine learning, the SaaS computer-implemented comprising:

• receiving a plurality of data points and a request for classification services from a remote user through a network connection;
• applying, by a local computer server, a semi-supervised k-means process to a first set of data points of the plurality of data points and a second set of data points of the plurality of data points, wherein
• the first set of data points is labeled with a first class of a plurality of classes, and
• the second set of data points is labeled with a second class of the plurality of classes, different from the first class;
• clustering the first set of data points and the second set of data points into a first plurality of clusters and a second plurality of clusters, respectively, using the semi-supervised k-means process;
• constructing multi-polytopes for the first plurality of clusters and the second plurality of clusters;
• training, based on the constructing of the multi-polytopes, a plurality of support vector machine (SVM) classifiers, wherein each SVM classifier of the plurality of SVM classifiers is trained on a respective pair of clusters within the first plurality of clusters and the second plurality of clusters;
• determining, using the trained plurality of SVM classifiers, separation hyperplanes for the first plurality of clusters and the second plurality of clusters;
• determining labels for each cluster of the first plurality of clusters and each cluster of the second plurality of clusters, based on the separation hyperplanes;
• and
• providing the determined labels for each cluster of the first plurality of clusters and each cluster of the second plurality of clusters, to the remote user.