Candidate ML Model Feature Adaptation for Faster Dataset Predictions
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Solution Overview
Problem
Current machine learning systems face inefficiencies and errors in feature extraction and model training, requiring extensive computational resources and time, especially with high-dimensional datasets, leading to inaccurate predictions and resource wastage.
Innovation Solution
Utilize transfer learning to select a candidate machine learning model from pre-trained models, apply it to a modified feature set based on an objective rules set, and generate performance optimization predictions for new datasets, minimizing retraining and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual feature extraction techniques are used to transform datasets into feature sets for machine learning models, then the models can be trained and deployed, but the process becomes computationally time-consuming, resource intensive, and error-prone
Solution Approach 1:
The patent applies preliminary action by pre-defining feature sets for different entity classifications before actual model training. These predefined feature sets are stored in a database and can be directly applied when new datasets need processing, eliminating the need for time-consuming manual feature extraction each time a model is trained.
Solution Approach 2:
The patent uses copying by creating standardized feature set templates for different entity classifications. Instead of extracting features manually each time, the system copies and applies the appropriate predefined feature set template to the new dataset, significantly reducing processing time while maintaining consistency and accuracy.
2Adaptability or versatility
If manual feature extraction and model training processes are used, then customized models can be created for specific datasets, but extensive computational resources and time are consumed
Solution Approach 1:
The patent implements universality by creating a library of predefined feature sets that can serve multiple purposes across different entity classifications. A single predefined feature set can be applied to multiple datasets within the same classification, eliminating the need to create custom feature extraction processes for each individual model while maintaining adaptability.
Solution Approach 2:
The patent applies parameter changes by allowing the system to select different predefined feature sets based on the entity classification parameter. This enables the system to adapt to different dataset types by simply changing which predefined feature set is applied, rather than performing extensive computational feature extraction for each case.
3Measurement precision
If traditional machine learning systems process high-dimensional datasets, then comprehensive analysis can be achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent applies the extraction principle by identifying and extracting only the most relevant features for each entity classification through predefined feature sets. Instead of processing all dimensions of high-dimensional datasets, the system extracts and applies only the necessary features that have been predetermined to be most useful for that classification type.
Solution Approach 2:
The patent uses segmentation by dividing the feature extraction process into distinct predefined feature sets for different entity classifications. This segmentation allows the system to handle high-dimensional datasets efficiently by applying the appropriate segmented feature set rather than processing all dimensions uniformly, improving both speed and relevance.
Data Source
AI summary
Embodiments of the present disclosure provide for providing performance optimization predictions related to an entity dataset. Such embodiments may include selecting a candidate machine learning model from a plurality of candidate machine learning models based at least in part on (i) a data profile for an entity dataset associated with an entity identifier and (ii) a predefined model profile associated with the candidate machine learning model. Such embodiments may additionally or alternatively include generating a candidate feature set by modifying a predefined feature set for the candidate machine learning model based at least in part on the data profile associated with the entity identifier. Such embodiments may additionally or alternatively include generating one or more performance optimization data objects by applying the candidate machine learning model to the candidate feature set based at least in part on an objective rules set for the entity identifier.