Neighbor Frequency Aggregation for Decision Tree Probability Estimation
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Solution Overview
Problem
Existing approaches to computing probability distributions for contextual bandit problems, particularly when dealing with decision trees, face challenges in accurately estimating posterior probabilities for data with complex dependencies and limited computational resources.
Innovation Solution
The method involves examining pairs of samples in each leaf node of a plurality of decision trees to determine neighbor frequencies, calculating an average neighbor frequency for each leaf node, and using these frequencies to aggregate parametric probability distributions across ensembles of decision trees, thereby improving the accuracy of probability estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to compute probability distributions for contextual bandit problems, then computational resources are consumed, but measurement precision of posterior probabilities deteriorates
Solution Approach 1:
The patent segments the computation of probability distributions by examining pairs of samples within leaf nodes of decision trees. Instead of computing distributions across entire datasets, the method divides computation into discrete leaf node pairs, calculating neighbor frequencies locally and aggregating results. This segmentation reduces overall computational resource consumption while maintaining estimation accuracy.
Solution Approach 2:
The patent changes the parameter representation from traditional probability distribution computations to neighbor frequency aggregations. By parameterizing the problem in terms of how often sample pairs appear together in leaf nodes across multiple decision trees, the method achieves more accurate posterior probability estimates with reduced computational overhead, particularly for contextual bandit problems.
2Measurement precision
If more computational resources are allocated to improve probability distribution accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent performs preliminary actions by pre-computing neighbor frequencies for sample pairs across multiple decision trees before solving the contextual bandit problem. These pre-computed frequencies are stored and reused during Thompson sampling, eliminating the need for repeated complex probability distribution computations and thereby improving processing efficiency without sacrificing accuracy.
Solution Approach 2:
The method creates simplified copies of probability distribution information in the form of neighbor frequency counts. Instead of working with complex continuous probability distributions, the patent uses discrete frequency copies that can be efficiently aggregated and used for decision-making, improving both accuracy and processing efficiency.
3Measurement precision
If existing methods are used for sparse data, then device complexity is reduced, but measurement precision of probability distributions deteriorates
Solution Approach 1:
The patent merges information from multiple decision trees by aggregating neighbor frequencies across an ensemble. For sparse data, this combining approach allows the system to accumulate sufficient statistical evidence to achieve accurate probability estimates, overcoming the limitations of individual trees that would otherwise fail with sparse data.
Data Source
AI summary
A method, system, and computer-usable medium are disclosed for, comprising: examining pairs of samples in each leaf node of a plurality of decision trees and for each pair of samples, determining a neighbor frequency of each pair of samples as a frequency of how often such pair of samples are filtered into a particular leaf node of the plurality of decision trees and for each leaf node of the plurality of decision trees, determining a neighbor frequency of the leaf node as an average of the neighbor frequencies of the samples of the leaf node.