RankBoost Automated Feature Selection for Ranking Models

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Solution Overview

Problem

The existing feature selection process for ranking models in information retrieval is manual, time-consuming, and prone to over-fitting, especially when dealing with large numbers of features, which affects the accuracy and computational efficiency of ranking models like RankNet.

Innovation Solution

An automated method using the RankBoost algorithm iteratively applies ranking candidates to a training data set, updating weight distributions to identify favored features and selecting a target feature set that maximizes performance gain, thereby reducing the need for manual intervention and improving feature selection efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual feature selection is used, then feature set can be carefully tuned, but the process is time-consuming and labor-intensive

Engineering Contradiction:

Improvefeature set tuning qualityVSAvoidfeature selection time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs automated feature selection using RankBoost algorithm that automatically evaluates and selects features without human intervention. The algorithm self-adjusts weight distributions and identifies optimal feature sets through iterative processing of training data, eliminating the need for manual feature tuning while maintaining high selection quality.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical feature selection process with an automated computational system. The RankBoost algorithm uses mathematical optimization and machine learning techniques to automatically determine feature weights and selections, substituting human expertise with algorithmic processing that is both consistent and time-efficient.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If more features are incorporated into ranking models, then ranking accuracy may improve, but the risk of over-fitting increases

Engineering Contradiction:

Improveranking accuracyVSAvoidover-fitting risk

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The algorithm dynamically adjusts the weight distribution parameter across different features during iterative processing. By changing weight parameters based on performance evaluation, the system automatically identifies which features contribute most to ranking accuracy and assigns appropriate weights, preventing over-fitting while maximizing accuracy through optimized parameter selection.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The RankBoost algorithm incorporates feedback mechanisms where the performance of feature combinations is continuously evaluated against training data. This feedback loop allows the algorithm to adjust feature selections and weight distributions in real-time, retaining only features that improve ranking accuracy while eliminating those that cause over-fitting.

Inventive Principle:

Principle #23Feedback

3Loss of information

If a large number of features are used, then more information is captured, but computational cost increases

Engineering Contradiction:

Improveinformation capture completenessVSAvoidcomputational cost

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The algorithm extracts and selects only the most relevant features from the complete feature set through iterative evaluation. By taking out and retaining only the essential features that contribute to accurate ranking, the system reduces computational complexity while maintaining information capture completeness, eliminating redundant features that would increase computational cost.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the effective number of active features by dynamically adjusting weight parameters. Features with low weights or zero weights are effectively removed from computation, allowing the system to process only the most informative features. This parameter-based feature reduction maintains information capture while significantly reducing computational energy requirements.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS8301638B2Automated feature selection based on rankboost for ranking

Publication Date: 2012.10.30 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

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.