Automated feature selection based on rankboost for ranking
First Claim
1. A computer implemented method used in a ranking algorithm, the method comprising:
- reiteratively applying a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order, wherein each iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate based on the ranking results, and updates the weight distribution to be used in a next iteration by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate, wherein the ranking result is determined based in part on building a histogram and determining an integral histogram associated with the histogram; and
inferring a target feature set from the favored ranking candidates identified in a plurality of iterations.
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Abstract
A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.
98 Citations
18 Claims
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1. A computer implemented method used in a ranking algorithm, the method comprising:
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reiteratively applying a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order, wherein each iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate based on the ranking results, and updates the weight distribution to be used in a next iteration by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate, wherein the ranking result is determined based in part on building a histogram and determining an integral histogram associated with the histogram; and inferring a target feature set from the favored ranking candidates identified in a plurality of iterations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A method for selecting the feature set for a ranking algorithm, the method comprising:
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constructing a set of ranking candidates using an initial set of features; applying each ranking candidate to a training data set comprising a plurality of ranking objects having a known pairwise ranking order and a weight distribution of ranking object pairs, each ranking candidate yielding a ranking result, wherein the ranking result is determined based in part on building a histogram and determining an integral histogram associated with the histogram; comparing the ranking results of the set of ranking candidates to identify a favored ranking candidate; analyzing the ranking result of the favored ranking candidate to identify ranking object pairs poorly ranked by the favored ranking candidate; adjusting the weight distribution by increasing the weights of the ranking object pairs poorly ranked by the favored ranking candidate; reiterating the above applying, comparing, analyzing, and adjusting, each iteration identifying a favored ranking candidate; and inferring a target feature set from the favored ranking candidates identified in previous iterations. - View Dependent Claims (17)
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18. One or more computer readable tangible physical memory devices having stored thereupon a plurality of instructions that, when executed by a processor, causes the processor to:
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reiteratively apply a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order, wherein each iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate based in part on building a histogram and determining an integral histogram associated with the histogram, identifies a favored ranking candidate based on the ranking results, and updates the weight distribution to be used in next iteration by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate; and prepare the favored ranking candidates identified in a plurality of iterations for inferring a target feature set therefrom.
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Specification