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Completing our feature selection process, we implement two final layers of refinement to ensure model stability. First, we apply Correlation-Based Pruning. Any redundant features with an inter-feature correlation coefficient above 0.8 are removed, using mutual information scores as a tie-breaker. This prevents our model from being over-fitted to repetitive data.
All four strategies: Mutual Information, RFE, SFM, and Correlation Filtering are then synthesized through a Majority Voting Mechanism. A feature is only retained if it is selected by at least three out of the four methods. Finally, weĦŻve built in a Fallback Mechanism to guarantee stability; if the voting result is too sparse, the system automatically selects the top 20% of features based on mutual information. This dual-layered approach ensures that the BiLSTM network receives only the most discriminative, high-quality data, which is fundamental to our performance. |
IEEE/ICACT20260103 Slide.10
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