Enhance RL with Dendritic Cell Algorithm
Budget: $30 – $250 USD
I'm seeking an expert to enhance the accuracy of the Dendritic Cell Algorithm (DCA) for adaptive signal categorization. The current system performs inconsistently across datasets and suffers from both false positives and false negatives during classification tasks.
The goal is to adapts more effectively to varying data characteristics, reduce prediction errors, nd achieve robust, accurate anomaly detection.
Key Objectives
Improve classification accuracy by reducing both false positives (FP) and false negatives (FN).
Dynamically optimize the signal mapping process within DCA
Maintain compatibility with multiple datasets (e.g., NSL-KDD, UNSW-NB15, Spambase).
Provide clear visual performance metrics (Accuracy, F1, AUC-ROC, Confusion Matrix).
Technical Requirements
Strong Python skills
Deep understanding of Dendritic Cell Algorithm (DCA)
Familiarity with data preprocessing, feature scaling, and signal transformation
The goal is to adapts more effectively to varying data characteristics, reduce prediction errors, nd achieve robust, accurate anomaly detection.
Key Objectives
Improve classification accuracy by reducing both false positives (FP) and false negatives (FN).
Dynamically optimize the signal mapping process within DCA
Maintain compatibility with multiple datasets (e.g., NSL-KDD, UNSW-NB15, Spambase).
Provide clear visual performance metrics (Accuracy, F1, AUC-ROC, Confusion Matrix).
Technical Requirements
Strong Python skills
Deep understanding of Dendritic Cell Algorithm (DCA)
Familiarity with data preprocessing, feature scaling, and signal transformation