Decision Tree Model Selection for Neural Network Accuracy
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Solution Overview
Problem
Existing machine learning models often exhibit suboptimal accuracy due to the lack of a comprehensive understanding of which models are most suitable for specific applications, necessitating intensive analytics that are not time-efficient.
Innovation Solution
A method involving generating a plurality of models, splitting a dataset into training and testing sets, constructing decision trees from these models, and deploying the decision tree with the highest accuracy indicator, utilizing backpropagation to enhance neural network accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple different machine learning algorithms are used to improve accuracy for different applications, then model accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the machine learning model into multiple perceptrons, where each perceptron is trained on a different subset of the training dataset. This segmentation allows the system to evaluate multiple algorithmic approaches simultaneously while maintaining manageable complexity through modular architecture. Each perceptron processes a portion of the data, and their combined results achieve high accuracy without requiring a single complex algorithm to handle all data.
2Measurement precision
If extensive analytics are performed to understand algorithm performance, then model accuracy is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-splitting the training dataset into multiple subsets before training begins. This preliminary organization of data allows for parallel training of multiple perceptrons, reducing the overall training time. By preparing the data structure in advance and distributing it across multiple processing units, the system avoids the time loss that would result from sequential processing or extensive post-training analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the performance of each perceptron is continuously evaluated during training. This feedback allows the system to identify underperforming perceptrons and adjust their training parameters or data distribution in real-time, improving overall model accuracy without requiring extensive external analytics. The feedback loop enables self-optimization that reduces the need for time-consuming external evaluation.
3Device complexity
If a single machine learning model is used to simplify the system, then device complexity is reduced, but measurement precision decreases
Solution Approach 1:
The patent applies universality by creating a multi-functional perceptron architecture where each perceptron can handle different types of data patterns and relationships. Rather than using a single specialized model, the system employs multiple perceptrons that collectively cover a broader range of predictive scenarios. This universal approach allows the system to maintain simplicity in individual components while achieving high precision through the combined functionality of multiple perceptrons working together on the same prediction task.
Data Source
AI summary
Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.