IoT intrusion detection ensemble methods hold promise for alleviating decision bias under long-tailed class distributions, where single models often fail to achieve minority-class recall. Nevertheless, existing works continue to confront three primary challenges: (1) class-agnostic fusion rules that ignore heterogeneity in feature distributions and model competence across attack types. (2) insufficient exploitation of complementary inductive biases between classifiers, limiting coverage of heterogeneous IoT traffic. and (3) computationally inefficient, noise-prone hyperparameter tuning in high-dimensional parameter spaces. Therefore, we propose a Leader-Guided Soft Voting Ensemble (LGSVE) model that integrates LightGBM, Random Forest, and Bi-Temporal Convolutional Networks to jointly capture structured feature patterns and long-range temporal dependencies. LGSVE employs a class-specific leader strategy combined with weighted soft voting to enhance robustness, particularly for minority-class detection. Furthermore, a Bayesian optimization framework is adopted to improve hyperparameter tuning efficiency and avoid premature convergence. Experiments on CIC-DDoS-2019, CIC-IDS-2017, and TonIot demonstrate that LGSVE significantly boosts minority-class performance while consistently surpassing state-of-the-art baselines in accuracy and robustness.