Automated Neural Network Pre-processor for Model Accuracy
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Solution Overview
Problem
Hiring data scientists to interpret and generate machine learning models is cost-prohibitive for many organizations, and existing advanced computing techniques like genetic algorithms and backpropagation are slow or inefficient, especially for large datasets or complex neural networks.
Innovation Solution
A pre-processor that automatically identifies machine learning problems and generates neural networks using a combination of genetic algorithms and selective backpropagation, enabling automated model building without prior knowledge of the problem type, thus facilitating data-driven model creation for regression, classification, and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genetic algorithms are used to generate neural networks, then model accuracy can be achieved, but the process takes a large number of iterations and consumes excessive time
Solution Approach 1:
The system performs preliminary data processing and feature extraction before neural network training, automatically identifying relevant features and preparing optimized training datasets. This preliminary action reduces the complexity of the learning task, enabling faster convergence while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts training parameters such as learning rate, batch size, and network architecture based on problem characteristics and data properties. This adaptive parameter adjustment optimizes training efficiency across different problem types without sacrificing model accuracy.
2Measurement precision
If backpropagation is used for training neural networks with large datasets or many nodes, then model accuracy can be achieved, but the process becomes slow
Solution Approach 1:
The system segments large datasets into manageable batches and divides complex training tasks across multiple processing units. This segmentation enables parallel processing of neural network training, significantly improving training speed while maintaining accuracy through coordinated optimization across segments.
Solution Approach 2:
The system replaces traditional sequential backpropagation with optimized algorithms including genetic algorithms for architecture search and parallel computing frameworks. This substitution eliminates the sequential bottleneck of traditional backpropagation, enabling faster training of large-scale neural networks.
3Adaptability or versatility
If multiple problem-specific machine learning mechanisms are used to handle different problem types, then appropriate solutions can be found, but the process becomes complex and difficult for inexperienced users
Solution Approach 1:
The system provides a universal automated model building engine that handles multiple problem types including classification, regression, and other machine learning tasks through a single unified interface. This universal system automatically adapts to different problem types without requiring users to select specific algorithms, reducing complexity while maintaining versatility.
Solution Approach 2:
The system performs automatic problem type detection and algorithm selection based on input data characteristics and desired outcomes. This self-service capability eliminates the need for users to understand different machine learning mechanisms, as the system automatically configures appropriate solutions for classification, regression, and other problem types.
4Measurement precision
If data scientists are hired to interpret data and construct models, then accurate models can be created, but the cost becomes prohibitive for many organizations
Solution Approach 1:
The system enables automated model building that performs data interpretation, feature selection, and neural network construction without requiring specialized data scientist expertise. This self-service capability allows organizations to build accurate models in-house, eliminating the need to hire expensive data scientists while maintaining model quality through automated optimization algorithms.
Data Source
AI summary
A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.