Iterative active feature extraction
First Claim
1. A method for feature extraction, the method comprising the steps of:
- a) receiving at least one query to predict at least one future value of a given value series;
b) generating a statistical model built on covariates to produce at least two predictions of the future value fulfilling at least the properties of
1) each being as statistically probable as possible given the statistical model wherein to be as statistically probable as possible an absolute distance of each of the predictions to a true value is less than a predetermined distance parameter with greater than a predetermined probability and
2) being mutually divert in terms of a numerical distance measure; and
c) querying a user to select one of the predictions.
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Abstract
Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.
10 Citations
19 Claims
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1. A method for feature extraction, the method comprising the steps of:
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a) receiving at least one query to predict at least one future value of a given value series; b) generating a statistical model built on covariates to produce at least two predictions of the future value fulfilling at least the properties of
1) each being as statistically probable as possible given the statistical model wherein to be as statistically probable as possible an absolute distance of each of the predictions to a true value is less than a predetermined distance parameter with greater than a predetermined probability and
2) being mutually divert in terms of a numerical distance measure; andc) querying a user to select one of the predictions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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Specification