Genetic Algorithm Prediction Function Factor Reduction
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Solution Overview
Problem
Existing data processing technologies face challenges in efficiently generating accurate functions with numerous variables, as the complexity and number of factors often exceed computational resources, particularly in applications like stock price prediction, where many correlated factors are considered.
Innovation Solution
The system employs a combination of principal component analysis (PCA) to reduce correlated factors into a smaller set of uncorrelated components and a genetic algorithm to generate a prediction function, prioritizing factors based on importance and grouping correlated factors, thereby reducing the number of variables needed for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous variables and factors are used in modeling functions, then the accuracy of results is improved, but the computational resources required and processing time increase
Solution Approach 1:
The patent extracts and removes irrelevant or redundant variables from the dataset using genetic algorithms. The system evaluates the importance of each variable and eliminates those that do not contribute significantly to prediction accuracy, thereby reducing computational complexity while maintaining model performance
Solution Approach 2:
The patent applies partial action by using a subset of the most important variables rather than all available variables. Genetic algorithms identify and retain only the critical factors needed for accurate predictions, avoiding the computational burden of processing excessive data
2Measurement precision
If numerous variables and factors are used in modeling functions, then the accuracy of results is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary variable selection using genetic algorithms before the actual modeling process. By pre-identifying and ranking the importance of variables, the system prepares an optimized subset of data that will be used in subsequent analysis, reducing processing time without sacrificing accuracy
Solution Approach 2:
The system uses only the necessary subset of variables identified through genetic algorithm analysis, avoiding processing of redundant data. This partial action approach significantly reduces computation time while maintaining predictive accuracy
3Measurement precision
If numerous variables and factors are used in modeling functions, then more accurate results are achieved, but the difficulty of determining the optimal function increases
Solution Approach 1:
The patent implements feedback mechanisms through genetic algorithms that evaluate the performance of different variable combinations. The system provides feedback on which variables contribute most to prediction accuracy and adjusts the variable subset accordingly, making the function determination process systematic rather than trial-and-error
Solution Approach 2:
The patent changes the parameter of variable importance by using genetic algorithms to dynamically assess and rank variables based on their contribution to the model. This transforms the static problem of selecting variables into a dynamic optimization process where variable importance is continuously evaluated and adjusted
Data Source
AI summary
A system to reduce the number of factors that need to be considered in generating a prediction function includes an access module and a function generator module. The access module accesses a reduced set of factors derived from an original set of factors based at least in part on correlations between the factors of the original set. The function generator module generates, based on the reduced set of factors and a data set associated therewith, a plurality of potential prediction functions that operate on the data set to predict a result, evaluates performance of each one from the plurality of potential prediction functions, and selects a solution prediction function based on the evaluated.