Multi-Sigma - Functions
Multi-input Multi-objective Analysis
Multi-Sigma predicts up to 100 objective variables from up to 200 explanatory variables, and it can reverse-analyze the 200 explanatory variables to satisfy the conditions of 100 objective variables simultaneously. Therefore, it is unlike general automated machine learning (AutoML) outputs, which only take one prediction function.
Predictive AI x Optimization AI
Multi-Sigma combines neural networks with Bayesian optimization – the two major techniques in deep learning. This allows the analysis to combine neural networks for structured data sets (> 20 samples) and Bayesian optimization for sequential parameter optimization starting from minimal data values (3 – 5 samples).
Predictive AI x Optimization AI
Multi-Sigma executes prediction using deep learning alongside optimization using genetic algorithms within a single environment. The optimization AI can automatically explore multi-objective optimal solutions while using predictive AI.
Precise Optimization
Multi-Sigma performs multi-objective optimization utilizing genetic algorithms and therefore allows maximization, minimization, and target value conditions of objective variables as well as the constraint conditions of explanatory variables.
Factor Analysis
By using techniques such as predictive model generation and sensitivity analysis, Multi-Sigma evaluates quantitatively the extent to which each explanatory variable contributes positively or negatively.
Auto-tuning of Hyperparameters
Multi-Sigma tunes hyperparameters automatically, which allows for high-accuracy prediction while controlling overfitting with a single mouse click.
AI Experimental Design Method
By transitioning from traditional statistical-based experimental design methods to AI-based experimental design methods, Multi-Sigma solves complex interaction problems, improves predictive accuracy, and achieves multi-objective prediction and optimization. This simplifies experimental design and eliminates unnecessary efforts.
Integration of Various Data
Using image analysis tools and parameter diversity handling tools, Multi-Sigma can handle diverse data, such as categorical variables and image data. This enables spectral decomposition and the breakdown of explanatory variables to solve complex problems.
Advanced Data Preprocessing Tools
Along with various preprocessing tools to enhance predictive accuracy, Multi-Sigma includes tools to enable high-accuracy prediction even for rare or biased data.