Labeling of data for machine learning
A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.
1. A computer system for generating labels for machine learning algorithms, the computer system comprising:
a memory; and
at least one processor circuit in communication with the memory, wherein the computer system is configured to perform a method, the method comprising:
retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data;
generating a graph that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets;
determining weights for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges;
applying an algorithm that creates grouped labels from the multiple label sets and based upon the weights for the plurality of edges;
identifying data points from the corpus of data that represent conflicts within the grouped labels;
generating a new label set based upon the grouped labels and further classification received from the entities; and
transmitting an electronic message for presenting the identified data points to entities for further classification.
2. The system of claim 1 , wherein the method further comprises training a machine learning algorithm using the new label set and the corpus of data.
3. The system of claim 1 , wherein the conflicts within the grouped labels include at least one data point that is in two different groups of labels.
4. The system of claim 1 , wherein the conflicts within the grouped labels include at least one data point that is not in any of the grouped labels.
5. The system of claim 1 , wherein the graph is a complete multipartite graph with respect to the label sets.
6. The system of claim 1 , wherein the algorithm is a hill climbing optimization algorithm that attempts to maximize the weights for the plurality of edges.
7. The system of claim 1 , wherein the method further comprises:
identifying conflicts in the new label set.
8. The system of claim 7 , wherein the method further comprises:
transmitting an electronic message based upon the conflicts in the new label set.
9. A 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:
retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data;
generating a graph that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets;
determining weights for the plurality of edges based upon a consistency between the data points classified by two labels connected by the edges;
applying an algorithm that creates grouped labels from the multiple label sets and based upon the weights for the plurality of edges; and
identifying data points from the corpus of data that represent conflicts within the grouped labels;
transmitting an electronic message for presenting the grouped data points to entities for further classification; and
generating a new label set based upon the grouped labels and further classification received from the entities.
10. The computer program product of claim 9 , wherein the method further comprises training a machine learning algorithm using the new label set and the corpus of data.
11. The computer program product of claim 9 , wherein the method further comprises identifying conflicts in the new label set.
12. The computer program product of claim 11 , wherein the method further comprises transmitting another electronic message based upon the conflicts in the new label set.
13. The computer program product of claim 9 , wherein the conflicts within the grouped labels include at least one data point that is in two different groups of labels.
14. The computer program product of claim 9 , wherein the conflicts within the grouped labels include at least one data point that is not in any of the grouped labels.
15. The computer program product of claim 9 , wherein the graph is a complete multipartite graph with respect to the label sets.
16. The computer program product of claim 9 , wherein the algorithm is a hill climbing optimization algorithm that attempts to maximize the weights for the plurality of edges.