Random Forest Classifier Flat Data Structure for Embedded Systems

The patent segments the random forest classifier into multiple independent decision trees, each handling a portion of the classification task. This segmentation allows the system to process data in smaller, more manageable units, reducing the memory footprint and processing overhead while maintaining overall classification accuracy through the collective decision-making of multiple trees.

The patent transforms the traditional if-else statement structure into a parametric evaluation model where decision rules are represented as mathematical functions with adjustable parameters. This parameterization enables more efficient computation by replacing complex conditional logic with streamlined mathematical operations, reducing processing resources while preserving classification performance.

The patent implements a simplified version of the random forest algorithm that uses a reduced number of decision trees and simplified evaluation rules compared to the traditional implementation. This partial action approach provides sufficient classification capability for embedded applications while dramatically reducing memory and processing requirements, making the system practical for resource-constrained environments.

The patent employs lightweight data structures and temporary variables that are created and discarded during the classification process, rather than maintaining complex persistent structures. This approach minimizes memory allocation and deallocation overhead, enabling the classifier to operate efficiently within the limited memory constraints of embedded systems.

The patent replaces the mechanical if-else statement evaluation mechanism with a parametric mathematical evaluation system. Instead of sequentially checking multiple conditional statements, the system uses mathematical functions to evaluate decision rules, which can be computed more efficiently and in parallel, thereby increasing throughput while maintaining classification accuracy.

The patent pre-computes and stores decision thresholds and parameter values during the training phase, so that during classification, the system only needs to perform simple parameter comparisons and mathematical evaluations rather than complex logical reasoning. This preliminary preparation significantly speeds up the classification process while preserving accuracy.