## 1 Introduction  
Ensemble learning has gained increasing prominence in machine learning in recent years, emerging as a pivotal tool for enhancing model performance. It refers to methodologies that combine predictions from multiple independent models to improve overall performance. While single models may underperform when addressing complex problems due to overfitting or inherent biases, the core philosophy of ensemble learning lies in leveraging model diversity to overcome performance bottlenecks caused by overfitting or bias in individual models, thereby achieving superior accuracy, generalization capability, and robustness compared to single models [1, 2]. With the advancement of deep learning, ensemble learning techniques and their application scenarios have continued to expand and evolve. Traditional majority voting, despite its simplicity, often delivers suboptimal results when handling complex or specialized data and tasks, necessitating more flexible and effective strategies to integrate the strengths of diverse models [3]. Ensemble learning is often regarded as the machine learning counterpart of the \"wisdom of crowds\" [4]. Conventional methods, such as majority voting and weighted averaging, operate under the assumption that the majority of predictions are correct. Random forest, for instance, exemplifies the effectiveness of majority voting [5]. While these methods perform well in many scenarios, their efficacy may diminish when individual classifiers exhibit significant diversity and minority classifiers demonstrate higher confidence in specific samples. This is particularly evident in cases requiring specialized knowledge to determine ground-truth answers, where the correct judgments of minority classifiers risk being overshadowed by erroneous predictions from the majority. Such limitations can lead to degraded overall performance. Recent advances in crowd wisdom theory have introduced novel perspectives for ensemble learning. To address the aforementioned constraints, we propose an ensemble strategy that integrates Bayesian Truth Serum (BTS) with the concept of surprise popularity [6; 7; 8]. BTS leverages two critical pieces of information: participants' direct answers to questions and their predictions about the group's responses. By computing the prior probability of events and deriving posterior probabilities for participants, BTS evaluates their credibility based on posterior probabilities and confidence levels. This approach diverges from traditional majority-centric methods by prioritizing participants who provide correct answers while accurately predicting others' responses, as such individuals are more likely to possess genuine expertise. Recent work by [PERSON] et al. [9] further extends this theory through a hierarchical Bayesian framework for collective decision-making. The surprise popularity algorithm shares similarities with BTS, as it also analyzes participants' answers and their inferences about group responses. For challenging, less-understood problems, this method identifies experts as those who exhibit high confidence in their answers and recognize that most others may fail to provide correct solutions. Under high-difficulty scenarios, the algorithm prioritizes opinions from such experts. Recent studies demonstrate its unique advantages in complex tasks like ranking problems [10]. Our strategy further incorporates classification confidence, which evaluates the reliability of each classifier's prediction for specific samples. By quantifying confidence levels, we determine which classifiers merit greater trust in their predictions [11]. When classifiers disagree on a sample, confidence metrics enable weighted aggregation of their judgments. In the context of surprise popularity, confidence also adjusts posterior probabilities to identify correct minority predictions, thereby enhancing the ensemble's ability to extract accurate results from minority classifiers.