Feature Preprocessing for Machine Learning Model Complexity Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models face challenges in reducing complexity and preventing overfitting due to the inclusion of unnecessary features, especially when the learning mechanism is unclear, leading to increased model complexity and the need for large datasets.
Innovation Solution
A method involving an autoencoder or Lasso regression model to identify feature influence and generate a sub-feature list, using correlation coefficients to select features for training, thereby reducing model complexity and time required for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all features are included in the training dataset, then the model may capture all potential patterns, but the model complexity increases and overfitting occurs
Solution Approach 1:
The patent extracts and removes unnecessary features from the training dataset using feature selection techniques. By identifying and eliminating redundant or irrelevant features, the model complexity is reduced while retaining only the most important features that contribute to prediction accuracy, thereby preventing overfitting.
Solution Approach 2:
The patent segments the feature set into important and unimportant features based on their contribution to the target variable. This segmentation allows the model to focus on relevant features while excluding unnecessary ones, achieving a balance between model complexity and predictive performance.
2Reliability
If all features are included in the training dataset, then comprehensive pattern recognition is possible, but the training time increases
Solution Approach 1:
The patent extracts and removes unnecessary features from the training dataset using feature selection techniques. By identifying and eliminating redundant or irrelevant features, the model complexity is reduced while retaining only the most important features that contribute to prediction accuracy, thereby preventing overfitting.
3Reliability
If a large amount of training data is collected without verifying feature independence, then more patterns may be learned, but the model complexity increases and overfitting occurs
Solution Approach 1:
The patent performs preliminary feature selection and independence verification before model training. By assessing feature importance and removing redundant features in advance, the dataset is preprocessed to contain only necessary features, thereby reducing model complexity and preventing overfitting before the actual training process begins.
Solution Approach 2:
The patent extracts and removes unnecessary features from the training dataset using feature selection techniques. By identifying and eliminating redundant or irrelevant features, the model complexity is reduced while retaining only the most important features that contribute to prediction accuracy, thereby preventing overfitting.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided is a method of preprocessing data for efficient machine learning. The method includes generating a feature prediction model based on a training dataset including a plurality of features of a target variable; generating, using the feature prediction model, a sub-feature list, which is a list of other features dependent on each feature constituting the training dataset; calculating correlation coefficients between the plurality of features and the target variable based on the training dataset; and selecting a feature to be used for training a model that predicts the target variable, from among the plurality of features based on the correlation coefficients and the sub-feature list.