Attribute Transformation for Random Forest Classifier Accuracy
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Solution Overview
Problem
Existing classification methods, such as random forest classifiers, face inefficiencies due to non-uniform data distributions in training databases, leading to computational resource waste and accuracy sacrifices, especially when dealing with attributes in different units of measurement.
Innovation Solution
A behavior detection module transforms attributes to achieve uniformity, interpolates missing values, and constructs a random forest classifier using the transformed database, ensuring all attributes are on the same unit and range, allowing for improved computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a grid of reference values is generated for each attribute to determine decision tree conditions, then the accuracy of the classifier is improved, but computational resources are wasted due to non-uniform data distribution
Solution Approach 1:
The patent applies parameter transformation to change the scale and distribution of attribute values. By transforming attributes to have uniform distributions and consistent scales, the method enables more efficient search for optimal split points without sacrificing classifier accuracy, thus resolving the contradiction between accuracy and computational efficiency
Solution Approach 2:
The patent performs preliminary transformation of the training data before constructing the decision trees. By pre-processing the attributes to achieve uniform distribution and consistent scaling, the method prepares the data in advance to avoid unnecessary computational iterations during the tree construction phase, reducing computational resource waste while maintaining accuracy
2Productivity
If the number of grid points is decreased to enhance computational efficiency, then computational resources are saved, but the accuracy of the classifier is sacrificed
Solution Approach 1:
The patent transforms the parameters (attribute values) to a uniform scale and distribution, which allows for fewer grid points to be used while maintaining the same level of accuracy. This parameter transformation enables the system to achieve both computational efficiency and classifier accuracy simultaneously
3Adaptability or versatility
If attributes with different units of measurement are used in classification, then the versatility of the classifier is improved, but it becomes difficult to compare distance metrics across different attributes
Solution Approach 1:
The patent applies parameter transformation to standardize the scale and units of different attributes. By transforming all attributes to a common scale and uniform distribution, the method enables meaningful comparison of distance metrics across attributes with originally different units, while preserving the diversity and information content of the original attributes
Solution Approach 2:
The patent creates equipotential conditions by transforming all attributes to the same scale and distribution. This ensures that each attribute contributes equally to the distance metric calculations, eliminating the disadvantage of different units and enabling fair comparison across all attributes
Data Source
AI summary
A behavior detection module receives a training database and applies a transformation to the attributes that improves the uniformity of the values associated with each attribute. The transformed training database is used to construct a random forest classifier (RFC). The RFC includes a plurality of decision trees and generates a classification label estimate for a data entry with a plurality of attributes. The classification label estimate is determined based on classification estimates from the plurality of decision trees. Each parent node of a decision tree is associated with a condition of a transformed attribute that directs the data entry to a corresponding child node depending on whether the condition is satisfied or not. The data entry is directed through the tree to one out of a set of leaf nodes, and a classification label associated with the leaf node.