IoT Feature Recommendation System Using Wavelet and Fourier Transform

The feature selection process is segmented into multiple independent algorithms (mRMR, mRMS, exhaustive search) that can be applied separately to different feature sets. Each algorithm handles specific aspects of feature selection, allowing parallel processing and reducing the time penalty of using multiple algorithms while maintaining high classification accuracy through comprehensive feature evaluation.

The system performs preliminary feature extraction using multiple algorithms before the actual classification task. By pre-selecting and ranking features using mRMR, mRMS, and exhaustive search methods, the system prepares optimized feature sets in advance, reducing the computational burden during real-time analytics and accelerating deployment while ensuring high accuracy.

The system implements self-service through automated feature selection algorithms that do not require domain expert intervention. The mRMR, mRMS, and exhaustive search algorithms automatically evaluate and select optimal features from sensor signals, replacing the need for expensive niche skill sets while maintaining high feature selection quality through mathematically rigorous optimization methods.

The patent replaces the mechanical system of human expert analysis with automated computational algorithms. Instead of relying on domain experts to manually analyze sensor signals and select features, the system uses algorithmic approaches (mRMR, mRMS, exhaustive search) to perform the same function with higher consistency and reduced cost, while the system complexity is managed through modular architecture.

The system extracts only the most relevant features from the complete set of sensor signal features using mRMR, mRMS, and exhaustive search algorithms. By identifying and extracting the subset of features that contribute most to classification performance while removing redundant and irrelevant features, the system achieves high classification accuracy with reduced computational burden and improved processing efficiency.

The system changes the parameter of feature dimensionality by transforming the original high-dimensional feature space into a lower-dimensional optimized feature set. Through parameter optimization in the feature selection process, the system identifies the optimal number and combination of features that maximize classification performance while minimizing computational resources, achieving the balance between reliability and productivity.