Data Labeling via Transformation Modules for ML Classification
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Solution Overview
Problem
Current data mining techniques face challenges in efficiently labeling data for trigger identification, particularly in vast datasets, where existing methods lack effectiveness in transforming and processing data for accurate classification and subsequent system triggering.
Innovation Solution
A system and method utilizing machine learning algorithms, specifically classification models trained with labeled data from clustering models, to transform, label, and trigger secondary systems, leveraging neural networks and Bayesian algorithms for improved data accessibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mining techniques are used for data labeling, then the process can handle basic datasets, but the effectiveness and accuracy deteriorate when dealing with vast datasets
Solution Approach 1:
The patent introduces transformation modules as intermediaries between the raw data and the classification model. These modules apply mathematical transformations (such as logarithmic, square root, or power transformations) to the data before classification, making the data more suitable for processing and improving labeling accuracy on vast datasets without requiring changes to the fundamental classification approach
Solution Approach 2:
The patent changes the parameters of the data by applying various transformations to modify the distribution, scale, or relationship of data points. This allows the classification model to work more effectively with transformed data, improving accuracy on large datasets while maintaining the same model architecture
2Measurement precision
If complex data transformation and classification processes are implemented, then data labeling accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial transformations by selecting and applying only certain transformation functions to specific datasets or data features, rather than applying all possible transformations. This reduces processing time while maintaining sufficient accuracy by focusing computational resources on the most beneficial transformations
Solution Approach 2:
The patent segments the data processing into distinct stages: initial data preparation, transformation application, classification, and post-processing. This segmentation allows for optimized processing at each stage and enables parallel processing of multiple transformations, reducing overall processing time while maintaining accuracy
3Ease of manufacture
If manual data labeling methods are used, then the process is simple to implement, but productivity and efficiency deteriorate with large volumes of data
Solution Approach 1:
The patent implements self-service by automating the data transformation and classification process. The system automatically selects appropriate transformations, applies them to the data, and generates labels without human intervention. This maintains implementation simplicity while dramatically increasing productivity, as the automated system can process vast amounts of data much faster than manual methods
Data Source
AI summary
A system and method is described for labelling data for trigger identification. The system and method comprising receiving data, transforming the data, extracting content from the data, processing data content through a classification machine learning model to receive a label, and trigger a secondary system based on the label. The system and method may further include maintaining a database of labeled data.