Hyperparameter Scaling Models for Faster Unsupervised Anomaly Detection
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Solution Overview
Problem
Unsupervised anomaly detection in machine learning (ML) faces challenges in selecting optimal hyperparameter values due to the absence of ground-truth labels, leading to inefficient and computationally expensive hyperparameter tuning processes that fail to exploit performance improvements.
Innovation Solution
A meta-learning approach for hyperparameter optimization using linear scaling models that predict optimal hyperparameter values based on dataset metafeatures, enabling fast and accurate selection without extensive training or evaluation, applicable to both unsupervised and supervised ML algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hyperparameter tuning is performed through greedy evaluation-based discovery, then model performance increases, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent pre-trains multiple anomaly detector models with different hyperparameter configurations on historical datasets before deployment. When a new dataset arrives, the system rapidly selects and adapts from these pre-configured models rather than performing extensive hyperparameter tuning from scratch, thus achieving high performance with reduced tuning time.
Solution Approach 2:
The system creates multiple copies of anomaly detector models with different hyperparameter settings trained on various historical datasets. These pre-trained model copies serve as ready-to-use configurations that can be quickly selected and adapted to new datasets, avoiding the need for time-consuming hyperparameter search while maintaining performance quality.
2Productivity
If default hyperparameter values are used to avoid tuning latency, then processing speed increases, but model performance deteriorates due to suboptimal configurations
Solution Approach 1:
The system automatically adjusts hyperparameter values based on characteristics of the input dataset. Instead of using fixed default values, the anomaly detector modifies parameters such as contamination ratio, metric selection, and model-specific hyperparameters according to dataset properties like size, dimensionality, and anomaly density, thereby achieving both speed and performance.
Solution Approach 2:
The hyperparameter configuration is made dynamic rather than static. The system adapts hyperparameter values in response to dataset characteristics, selecting different pre-trained model configurations based on real-time analysis of the input data properties, thus optimizing performance for each specific dataset without requiring manual tuning.
3Reliability
If multiple anomaly detector models with different hyperparameter configurations are evaluated, then detection accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The system segments the hyperparameter space into distinct configurations, with each segment corresponding to a specific pre-trained anomaly detector model. By dividing the complex hyperparameter optimization problem into manageable pre-configured segments, the system can efficiently select appropriate models based on dataset characteristics without evaluating all possible configurations.
Solution Approach 2:
Multiple anomaly detector models with different hyperparameter configurations are trained on diverse historical datasets to create a universal ensemble. This multi-functional model set can handle various dataset types and anomaly patterns, allowing the system to achieve high detection accuracy across different domains while maintaining a manageable number of pre-trained configurations.
Data Source
AI summary
A computer sorts empirical validation scores of validated training scenarios of an anomaly detector. Each training scenario has a dataset to train an instance of the anomaly detector that is configured with values for hyperparameters. Each dataset has values for metafeatures. For each predefined ranking percentage, a subset of best training scenarios is selected that consists of the ranking percentage of validated training scenarios having the highest empirical validation scores. Linear optimizers train to infer a value for a hyperparameter. Into many distinct unvalidated training scenarios, a scenario is generated that has metafeatures values and hyperparameters values that contains the value inferred for that hyperparameter by a linear optimizer. For each unvalidated training scenario, a validation score is inferred. A best linear optimizer is selected having a highest combined inferred validation score. For a new dataset, the best linear optimizer infers a value of that hyperparameter.