Local Complexity Mapping for Input-Output Dataset Quality
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Solution Overview
Problem
Existing machine-learning algorithms face challenges in accurately assessing the quality of training data due to uneven sampling of the input space and potential corruption of output datapoints, which can lead to increased uncertainty and inaccuracy, particularly in safety-critical applications like autonomous vehicles.
Innovation Solution
A method for assessing datasets using local complexity indicators and an array data structure that determines local complexity values for neighborhoods of varying sizes, allowing for efficient evaluation of dataset quality and identifying abnormalities, outliers, and ensuring appropriate sampling of the input space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of datasets is performed, then assessment precision is improved, but productivity deteriorates due to the large size of training data
Solution Approach 1:
The system enables self-assessment of dataset quality through automated computation of local complexity indicators and generation of assessment reports. The dataset itself provides the information needed for quality assessment through its structural properties (distances between input and output datapoints), eliminating the need for manual inspection while maintaining comprehensive evaluation capability.
Solution Approach 2:
Manual mechanical inspection of datasets is replaced by an automated computational system that calculates local complexity indicators using mathematical formulations. The system substitutes human manual checking with algorithmic processing that computes metrics like input distance, output distance, and their ratios to assess data quality objectively and efficiently.
2Reliability
If comprehensive assessment of all datasets is performed, then reliability is improved, but device complexity increases
Solution Approach 1:
The assessment system segments the comprehensive evaluation into manageable components by calculating local complexity indicators for individual neighborhoods around each datapoint. Instead of analyzing the entire dataset at once, the system divides the input space into local regions and assesses each region's complexity independently, then aggregates results to provide overall reliability assessment.
Solution Approach 2:
The system transforms the complex multi-dimensional problem of comprehensive dataset assessment into a series of simpler one-dimensional calculations. By computing the ratio of input distance to output distance for each datapoint and aggregating these scalar values into local complexity indicators, the system reduces dimensional complexity while maintaining comprehensive evaluation coverage.
3Measurement precision
If local complexity indicators are calculated for all neighborhoods, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial action by calculating local complexity indicators for a representative sample of neighborhoods rather than exhaustively analyzing every possible neighborhood. The method computes indicators for k nearest neighbors where k is a manageable number, providing sufficient precision for reliability assessment without requiring complete enumeration of all potential neighborhoods, thus balancing accuracy with computational time.
Data Source
AI summary
In a computer-implemented method of enabling an assessment of a plurality of datasets, the datasets each includes an input datapoint and an associated output datapoint. The plurality of datasets can be part of training data or validation data of a machine-learning algorithm such as a neural network. For each dataset multiple local complexity indicators are determined for multiple neighborhoods in the input space. A data structure is constructed that keeps track of the local complexity indicators.