GENERALIZED ACTIVE LEARNING
First Claim
1. A method for active learning that includes decisions on information acquisition of both missing labels and missing features within one or more cases, executed via a processor on a computer comprising a memory whereon computer-executable instructions comprising the method are stored, the method comprising:
- modeling a joint distribution of variables, comprising observed and unobserved labels and features, for one or more cases;
determining probability distributions for respective unobserved variables;
identifying an unobserved variable from the joint distribution of variables that has a return on information (ROI) metric corresponding to a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable;
observing the value of the identified variable; and
updating the probability distributions for the respective unobserved variables in the joint distribution of variables utilizing the value of the identified variable.
2 Assignments
0 Petitions
Accused Products
Abstract
Active learning is extended to decisions on information acquisition of both missing labels and missing features within one or more cases. In one example, desired (e.g., optimal) information to acquire about a case at hand and about cases in a training library during diagnostic sessions can be computed concurrently. A joint distribution of variables, comprising observed and unobserved labels and features for one or more cases, is modeled and probability distributions are determined for unobserved variables. An unobserved variable is selected from the joint distribution that has a return on information (ROI) metric having a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable. The value of the variable is observed, and the probability distributions for the respective unobserved variables in the joint distribution are updated using the value of the identified variable.
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Citations
20 Claims
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1. A method for active learning that includes decisions on information acquisition of both missing labels and missing features within one or more cases, executed via a processor on a computer comprising a memory whereon computer-executable instructions comprising the method are stored, the method comprising:
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modeling a joint distribution of variables, comprising observed and unobserved labels and features, for one or more cases; determining probability distributions for respective unobserved variables; identifying an unobserved variable from the joint distribution of variables that has a return on information (ROI) metric corresponding to a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable; observing the value of the identified variable; and updating the probability distributions for the respective unobserved variables in the joint distribution of variables utilizing the value of the identified variable. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A system for active learning that includes decisions on information acquisition of both missing labels and missing features within one or more cases, comprising:
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a variable modeling component configured to model a joint distribution of variables as an undirected graphical model, where the joint distribution of variables comprise observed and unobserved labels and features for one or more cases; a probability distribution determination component configured to determine probability distributions for the respective unobserved variables in the joint distribution of variables; a variable identification component configured to identify an unobserved variable from the joint distribution of variables that has a return on information (ROI) metric corresponding to a combination of a desired uncertainty metric for a value of the unobserved variable and a desired cost for observing the value of the unobserved variable; a value observation component configured to observe the value of the identified variable; and a probability distribution updating component configured to update the probability distributions for the respective unobserved variables in the joint distribution of variables utilizing the value of the identified variable. - View Dependent Claims (15, 16, 17, 18)
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19. A method for using an expected value of information to compute a desired next piece of information to gather about one or more diagnostic cases, executed via a processor on a computer comprising a memory whereon computer-executable instructions comprising the method are stored, comprising:
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comparing an expected value of acquiring information on extensions to a case library of training data and information known about one or more cases; and determining a desired next piece of information for the one or more diagnostic cases based on the comparison.
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20. A method for active learning that includes decisions on information acquisition of both missing labels and missing features within one or more cases, executed via a processor on a computer comprising a memory whereon computer-executable instructions comprising the method are stored, the method comprising:
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modeling a joint distribution of variables, comprising observed and unobserved labels and features, for one or more cases as an undirected graphical model; determining probability distributions for respective unobserved variables; creating a predictive model for unobserved features and labels using the probability distributions for respective unobserved variables for the undirected graphical model of the joint distribution of variables; identifying the unobserved variable comprising selecting the unobserved variable that has a desired return on information (ROI) metric, comprising; determining an uncertainty metric for the unobserved variable comprising determining a probability of an unobserved variable from a case given a set of observed variables for the case in the joint distribution of variables; determining the cost for observing the value of the unobserved variable comprising; defining a set of cost related parameters; determining a value for the respective cost related parameters for observing the value of the unobserved variable; and combining the respective cost related parameters'"'"' values to determine the cost for observing the value of the unobserved variable; and determining a ROI metric comprising comparing the uncertainty metric for the unobserved variable to the cost for observing the value of the unobserved variable; observing the value of the identified variable, comprising; performing a test to determine the value of the identified variable; and using an information source having a known value for the identified variable; and updating the probability distributions for the respective unobserved variables in the joint distribution of variables utilizing the value of the identified variable.
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Specification