Machine Learning Data Search With Loss-Based Relevance Evaluation
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Solution Overview
Problem
Existing data search services for machine learning tasks lack the ability to evaluate the relevance of search results to specific tasks, leading to inefficient data selection and potential costly purchases of irrelevant data sets.
Innovation Solution
A method that generates a search request with a client data set, characterizes objects by a feature set, determines prediction functions and loss functions, and compares these values to assess relevance, providing a response with relevant data owners and their calculated loss function values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a client uses existing data search services like Google Dataset Search or Dawex to find data sets for machine learning tasks, then the client can access a wide range of data sets from various owners, but the client cannot evaluate the relevance of the found data sets to their specific machine learning task before downloading or purchasing them
Solution Approach 1:
The system performs preliminary relevance evaluation by calculating loss function values for prediction models trained on candidate data sets before the client downloads or purchases them. This advance assessment allows clients to identify relevant data sets without incurring download or purchase costs for irrelevant data.
Solution Approach 2:
The system introduces an intermediary relevance evaluation mechanism that acts as a mediator between the client's machine learning task and the data set owners' data sets. This intermediary calculates loss function values to objectively assess relevance, eliminating the need for clients to manually evaluate each data set.
2Measurement precision
If a client downloads and tests multiple data sets to evaluate their relevance to a machine learning task, then the client can accurately assess which data sets are relevant, but the client spends significant time and potentially incurs costs for downloading irrelevant data
Solution Approach 1:
The system performs the time-consuming relevance evaluation in advance by calculating loss function values for candidate data sets before the client needs to download them. This preliminary assessment eliminates the need for clients to spend time downloading and manually testing multiple data sets.
Solution Approach 2:
The system replaces the manual mechanical process of downloading and testing data sets with an automated computational system that calculates loss function values. This substitution eliminates the need for clients to physically download and manually evaluate each data set, significantly reducing time consumption.
3Productivity
If a client purchases data sets without prior relevance evaluation, then the client can quickly access the data, but the client may waste money on irrelevant data sets
Solution Approach 1:
The system performs preliminary relevance evaluation by calculating loss function values before the client makes purchasing decisions. This advance assessment ensures that clients only purchase data sets that are proven to be relevant to their machine learning tasks, preventing waste of money on irrelevant data.
Solution Approach 2:
The system provides feedback to clients in the form of loss function values that indicate the relevance of candidate data sets to their specific machine learning task. This feedback mechanism guides clients' purchasing decisions, ensuring they invest in data sets that will actually improve their model performance.
Data Source
AI summary
A method for searching data for machine learning tasks is provided, comprising: generating a search request at a client data node, the search request comprising a client data set having objects and their labels; and sending the search request to an owner data node; and performing, by the client data node, the following operations: (a) characterizing the objects by a feature set comprising at least one feature from an owner data set, (b) determining a first prediction function of the feature set and the labels, (c) calculating a value of at least one loss function for the first prediction function; (d) determining a second prediction function of the features from the client data set and the labels; (e) calculating a value of at least one loss function for the second prediction function; and (f) generating a response to the received search request, the response comprising information on at least one data owner.