Feature Selection via Dynamic Priority Ranking and Feedback
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Solution Overview
Problem
Existing methods for selecting important features in machine learning models are inefficient, often requiring a large number of questions to align with user knowledge, as they may not consistently match on-the-spot knowledge, leading to inconsistent feature selection and increased questioning.
Innovation Solution
An information processing program that prioritizes feature questioning based on estimated importance scores, updating these scores based on user feedback, to efficiently select features consistent with user knowledge by ranking variables and adjusting weights according to user validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If features are selected based on statistical measures (correlation, mutual information, chi-square) or machine learning model impact, then feature selection is automated and systematic, but the number of questions required increases because these methods do not consistently match user's on-the-spot knowledge
Solution Approach 1:
The system implements feedback by updating the estimated amount based on user answer results. When users answer questions about feature importance, the system uses these answers to update the estimated importance scores, creating a closed-loop feedback mechanism that progressively refines feature selection to better match user knowledge
Solution Approach 2:
The feature selection process is made dynamic by allowing the order of priority and estimated amount to change based on user feedback. The system adapts the questioning sequence in real-time, adjusting which features are questioned next based on accumulated user responses rather than following a fixed static order
2Productivity
If the maximum possible number of important features are obtained with as few questions as possible, then user satisfaction increases, but the complexity of selecting and prioritizing features increases
Solution Approach 1:
The system performs preliminary action by pre-calculating the order of priority for features before the interactive questioning begins. This preliminary ranking based on statistical measures and model impact provides a starting point that guides the questioning process, reducing the need for exhaustive questioning while maintaining efficiency
Solution Approach 2:
The estimated amount serves as an intermediary that bridges automated feature selection and user knowledge. This intermediate metric combines objective statistical measures with subjective user feedback, allowing the system to balance automation with user satisfaction without requiring complete manual evaluation of all features
Data Source
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AI summary
An information processing program causes a computer(10) to execute a process including, deciding on one or more variables, from among a plurality of variables, to be a target for a question regarding degree of importance, based on order of priority of the variables and estimated amount, the order of priority of the variables being determined based on a plurality of patterns indicating ranking of the plurality of variables, the estimated amount indicating possibility of a match with predetermined condition regarding each of the patterns, (S103) and updating the estimated amount based on an answer result of the question about the decided variable.(S105,S106)