A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system for merging feature graphs, the computer system comprising: one or more computer processors; one or more computer-readable storage media; program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: instructions to receive a plurality of individual subsets of features of a dataset comprising multiple features, each subset represented as a graph based on a predefined template, and instructions to merge the plurality of graphs by combining elements of the graphs to form a merged feature graph for use in identifying a single subset of features. 2. The system of claim 1, wherein the predefined graph template comprises a simple graph comprising nodes that represent features in the subset and edges between pairs of nodes, wherein the system further comprises: instructions to combine overlapping nodes from different graphs into a merged node, wherein the overlapping nodes from different graphs represent the same feature in the dataset, and instructions to combine overlapping edges from different graphs into a merged edge, wherein the overlapping edges from different graphs extend between a pair of nodes representing the same pair of features in the dataset. 3. The system of claim 2, wherein the predefined graph template comprises a weighted graph, wherein each of the edges has a respective edge weight representing a relationship between the feature pair represented by the pair of nodes connected by the corresponding edge, wherein the system further comprises: instructions to combine the edge weights of overlapping edges from different graphs to determine a merged edge weight for each merged edge of the merged feature graph. 4. The system of claim 1, wherein the predefined graph template comprises a simple undirected graph comprising nodes that represent the features in the subset and edges between pairs of nodes, wherein the system further comprises: instructions to create said graph, for each of the received plurality of individual subsets, by mapping each feature of the individual subset to a node, and connecting at least some pairs of nodes by an edge. 5. The system of claim 1, wherein a received subset of features includes relevance data representing relevance or relative relevance of features within the subset, wherein the system further comprises: instructions to process the relevance data associated with the features of the received subset of features to determine an edge weight for each of the edges of the graph, wherein the edge weight represents a relationship between the feature pair represented by the pair of nodes connected by the corresponding edge. 6. The system of claim 5, wherein the relevance data comprise, or may be used to derive, a distance between a pair of features in the subset of features, wherein the system further comprises: instructions to determine an edge weight for an edge between a pair of nodes representing a feature pair based on a formula in which the edge weight is inversely proportional to the distance between the feature pair. 7. The system of claim 5, wherein the relevance data comprise, or may be used to derive, a ranking of each feature of the subset according to the relevance thereof, wherein the system further comprises: instructions to determine an edge weight for an edge between a pair of nodes representing a feature pair is determined using the formula: Edge Weight=[1/(1+Pos. Diff)]*[W*ê(−Decay/5)] Wherein Pos. Diff is the difference in position between the feature pair in the feature ranking; W is a weight for decay, and Decay is top position of any two connected features in the feature ranking, starting from 0. 8. The system of claim 1, further comprising: using the merged feature graph to identify the single subset of features by selecting a subset of features from the merged feature graph based on one or more of: all of the features represented by nodes in the merged feature graph; a threshold number of the most relevant features represented by nodes in the merged feature graph; all features represented by nodes in the merged feature graph meeting a threshold relevance value; all features of feature pairs represented by edges in the merged feature graph meeting a threshold merged edge weight, and features represented by nodes or connected by edges in the merged feature graph meeting any other suitable threshold. 9. The system of claim 1, further comprising: instructions to present a visualized representation of the merged feature graph, to enable user selection of the single subset of features. 10. A computer system comprising: one or more computer processors; one or more computer-readable storage media; program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: instructions to receive a plurality of individual subsets of features of a dataset of features and, for each individual subset of features, instructions to create a graph based on a predefined template for representing the subset of features; instructions to merge the plurality of graphs to form a merged feature graph; and instructions to determine relevance or relative relevance of the features represented by nodes in the merged feature graph for use in identifying a single subset of features. 11. A computer program product for merging feature graphs, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: instructions to receive a plurality of individual subsets of features of a dataset comprising multiple features, each subset represented as a graph based on a predefined template, and instructions to merge the plurality of graphs by combining elements of the graphs to form a merged feature graph for use in identifying a single subset of features. 12. The computer program product of claim 11, wherein the predefined graph template comprises a simple graph comprising nodes that represent features in the subset and edges between pairs of nodes, wherein the system further comprises: instructions to combine overlapping nodes from different graphs into a merged node, wherein the overlapping nodes from different graphs represent the same feature in the dataset, and instructions to combine overlapping edges from different graphs into a merged edge, wherein the overlapping edges from different graphs extend between a pair of nodes representing the same pair of features in the dataset. 13. The computer program product of claim 12, wherein the predefined graph template comprises a weighted graph, wherein each of the edges has a respective edge weight representing a relationship between the feature pair represented by the pair of nodes connected by the corresponding edge, wherein the system further comprises: instructions to combine the edge weights of overlapping edges from different graphs to determine a merged edge weight for each merged edge of the merged feature graph. 14. The computer program product of claim 11, wherein the predefined graph template comprises a simple undirected graph comprising nodes that represent the features in the subset and edges between pairs of nodes, wherein the system further comprises: instructions to create said graph, for each of the received plurality of individual subsets, by mapping each feature of the individual subset to a node, and connecting at least some pairs of nodes by an edge. 15. The computer program product of claim 11, wherein a received subset of features includes relevance data representing relevance or relative relevance of features within the subset, wherein the system further comprises: instructions to process the relevance data associated with the features of the received subset of features to determine an edge weight for each of the edges of the graph, wherein the edge weight represents a relationship between the feature pair represented by the pair of nodes connected by the corresponding edge. 16. The computer program product of claim 15, wherein the relevance data comprise, or may be used to derive, a distance between a pair of features in the subset of features, wherein the system further comprises: instructions to determine an edge weight for an edge between a pair of nodes representing a feature pair based on a formula in which the edge weight is inversely proportional to the distance between the feature pair. 17. The computer program product of claim 15, wherein the relevance data comprise, or may be used to derive, a ranking of each feature of the subset according to the relevance thereof, wherein the system further comprises: instructions to determine an edge weight for an edge between a pair of nodes representing a feature pair is determined using the formula: Edge Weight=[1/(1+Pos. Diff)]*[W*ê(−Decay/5)] Wherein Pos. Diff is the difference in position between the feature pair in the feature ranking; W is a weight for decay, and Decay is top position of any two connected features in the feature ranking, starting from 0. 18. The computer program product of claim 11, further comprising: using the merged feature graph to identify the single subset of features by selecting a subset of features from the merged feature graph based on one or more of: all of the features represented by nodes in the merged feature graph; a threshold number of the most relevant features represented by nodes in the merged feature graph; all features represented by nodes in the merged feature graph meeting a threshold relevance value; all features of feature pairs represented by edges in the merged feature graph meeting a threshold merged edge weight, and features represented by nodes or connected by edges in the merged feature graph meeting any other suitable threshold. 19. The computer program product of claim 11, further comprising: instructions to present a visualized representation of the merged feature graph, to enable user selection of the single subset of features. 20. A computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: instructions to receive a plurality of individual subsets of features of a dataset of features and, for each individual subset of features, instructions to create a graph based on a predefined template for representing the subset of features; instructions to merge the plurality of graphs to form a merged feature graph; and instructions to determine relevance or relative relevance of the features represented by nodes in the merged feature graph for use in identifying a single subset of features.