Model Analyzer for Algorithm Selection Based on Data Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for selecting the best anomaly detection algorithm for a data set are time-consuming and ineffective, as the performance of an algorithm on a partial data set may not match its performance on the complete data set due to differences in characteristics, and existing approaches do not efficiently determine the suitability of machine learning models based on data distribution.
Innovation Solution
A system that selects an analytic model based on the distribution type of a data set by using a Model Analyzer with components like Distribution Analyzer, Model Filter, Model Trainer, and Model Evaluator to identify suitable machine learning models, train them on a representative data set, and evaluate their accuracy, thereby avoiding the training of unsuitable models and selecting the most accurate one for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each algorithm is executed on a partial data set to evaluate performance before using it on the complete data set, then the algorithm selection may be more accurate, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent applies preliminary action by analyzing the distribution characteristics of the data set before selecting algorithms, and by evaluating algorithms on a carefully selected representative subset rather than the complete data set. This preliminary distribution analysis and selective subset evaluation prepares the ground for efficient algorithm selection without requiring exhaustive testing on all data, thus reducing time while maintaining selection accuracy.
Solution Approach 2:
The patent uses a representative subset that copies the essential distribution characteristics of the complete data set. Instead of evaluating algorithms on the entire data set, a smaller copy (subset) with matching distribution properties is used for evaluation, which significantly reduces computation time while preserving the accuracy of performance assessment.
2Productivity
If algorithms are selected without considering data distribution characteristics, then the selection process is simpler and faster, but the algorithm performance may be suboptimal due to mismatch between algorithm assumptions and data characteristics
Solution Approach 1:
The patent applies parameter changes by using distribution analysis to identify key characteristics (parameters) of the data set such as distribution type, skewness, and kurtosis. These identified parameters then guide the selection of algorithms whose assumptions match the data characteristics, ensuring both efficiency and reliability in algorithm selection.
Solution Approach 2:
The patent introduces distribution analysis as an intermediary step between the raw data and algorithm selection. This intermediary analysis characterizes the data distribution and uses those characteristics to mediate the selection process, matching algorithms to data types. This intermediary layer ensures that selection is both efficient (by providing clear selection criteria) and reliable (by matching algorithm assumptions with data realities).
3Productivity
If a representative subset is used to evaluate algorithm performance instead of the complete data set, then the evaluation is faster and more efficient, but there is a risk that performance on the subset may not match performance on the complete data set
Solution Approach 1:
The patent applies local quality by ensuring that the representative subset preserves the local distribution characteristics (quality) of the complete data set. The subset is specifically constructed to match the distribution properties of the full data, so that algorithms evaluated on the subset will perform similarly on the complete data set, maintaining measurement precision while improving evaluation speed.
Data Source
AI summary
A model analyzer may receive a representative data set as input and select one of a plurality of analytic models to perform the analysis. Before deciding which model to use the model may be trained, and the trained model evaluated for accuracy. However, some models are known to behave poorly when the training data is distributed in a particular way. Thus, the cost of training a model and evaluating the trained model can be avoided by first analyzing the distribution of the representative data. Identifying the representative data distribution allows ruling out use of models for which the distribution of the representative data is unsuitable. Only models that may be compatible with the distribution of the representative data may be trained and evaluated for accuracy. The most accurate trained model whose accuracy meets an accuracy threshold may be selected to analyze subsequently received data related to the representative data.