Probabilistic Sampling for Machine Learning Dataset Generation
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Solution Overview
Problem
Existing machine learning model training methods, such as reinforcement learning and generative adversarial networks, face challenges in generating suitable datasets, particularly when datasets are biased or inadequate, leading to overfitting and underfitting, and the inability to handle diverse scenarios effectively.
Innovation Solution
A method for generating a balanced training dataset through probabilistic sampling, where the probability of selecting samples is inversely proportional to their frequency, ensuring a diverse representation of behaviors, and weighting to emphasize important actions, thereby reducing oversampling and undersampling of behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If imitation learning uses a large dataset to train the model to react appropriately in a wide range of scenarios, then the model's adaptability improves, but the data processing complexity and storage requirements increase significantly
Solution Approach 1:
The patent extracts and separates the dataset generation process from the model training process. A separate dataset generation system creates synthetic training data with controlled diversity, which is then used to train the imitation learning model. This extraction allows the model to achieve high adaptability without requiring access to or processing of excessively large real-world datasets.
Solution Approach 2:
The patent applies preliminary action by generating and preparing diverse training datasets before the actual model training occurs. The system pre-generates synthetic experiences covering a wide range of scenarios, ensuring the model is exposed to diverse situations during training without requiring complex real-time data processing during deployment.
2Quantity of substance
If the training dataset includes poor-quality data, then the dataset size increases, but the model performance deteriorates due to overfitting with some scenarios and underfitting in others
Solution Approach 1:
The patent changes the parameters of data generation by using synthetic environments with controlled variables. Instead of using raw real-world data with unknown quality variations, the system generates training data with explicitly controlled parameters for scenario diversity, ensuring consistent and reliable model performance across different situations without requiring filtering of poor-quality data.
3Manufacturing precision
If reinforcement learning is used with well-defined rewards at regular intervals, then the training effectiveness improves, but the applicability to continuous behavior learning deteriorates
Solution Approach 1:
The patent introduces an intermediary approach by combining elements of both reinforcement learning and imitation learning. The system uses a critic network that provides continuous feedback signals (intermediary between discrete rewards and continuous behavior), allowing the model to learn continuous behaviors while maintaining training effectiveness through structured reward-like signals.
Data Source
AI summary
A system for generating a training dataset for a machine learning process, and training a machine learning model, the system comprising a data obtaining unit configured to obtain training data comprising a plurality of events of interest and the behaviour of an agent corresponding to those events, an event identifying unit configured to identify, based upon one or more corresponding indicators, the occurrence of an event of interest in the training data, a list generating unit configured to generate a list of identified events in the training data, wherein identified events are added to the list with a probability that is inversely proportional to the frequency of the occurrence of that event within the training data, a dataset generating unit configured to generate a dataset comprising information about the events contained in the generated list, and a training unit configured to train a machine learning model using the generated dataset, wherein the machine learning model is trained to generate behaviour for an agent corresponding to events within the generated dataset.