## 2 Background
### Crowd Selection in Crowdsourcing
#### 2.1.2 Experience-Based Crowd Selection  
The previous method uses basic approaches which are widely supported by crowdsourcing platforms. However, they possess inherent constraints which can limit their effectiveness [25]. In addition, such approaches might not fit all kinds of tasks [17]. More reliable crowd selection approaches consider using historical or current data about crowd workers' performance to estimate their performance on a future task [25]. Crowdsourcing platforms allow choosing crowd workers based on the information available during registration such as a brief description of their skill set and knowledge. However, it is of paramount importance to discover the latent features that are learned from historical data of the crowd [26]. Usually, to infer crowd workers' experience, studies have presented approaches utilizing intelligent techniques rather than basic filtering and screening. Regardless of the different labels used by studies such as capability, performance and knowledge, we believe they all form experience. Therefore, we use the general term \"experience\" to indicate all relevant terms. There are numerous studies that have investigated crowd selection based on crowd workers' experience, knowledge and performance. Indeed, crowd workers are more likely to perform well in tasks they have previously worked on [27]. For microtasks,Carminati et al. [28] have proposed a crowd selection approach called CrowdSelect which selects the best crowd that would yield high-quality responses with the lowest error in a certain domain. It predicts the crowd worker's error rate while completing a task, and based on his historical record, the error rate of the task ahead is predicted using an auto-regressive moving average model. In addition, [PERSON] et al. [29] have proposed a crowd selection approach leveraging history records of the workers. Their proposed algorithm clusters the crowd workers' history tasks and for each cluster it learns the crowd workers' features which optimize their performance in a task. When a new task is started, its features are extracted and matched with these clusters to find the best crowd workers for that task. Furthermore, [PERSON] et al. [30] proposed an approach for Selecting Workers Wisely for Crowdsourcing (SWWC). It aims to select crowd workers who possess the required domain expertise in a task's domain knowledge and as well detect copiers to eliminate them from participating in a task. Credibility of crowd workers in a domain is calculated using historical task records or initial registration information if the worker is new and a greedy algorithm was utilized to effectively solve the crowd selection approach. Another study by [PERSON] et al. [31] focused on selecting expert crowd workers by employing belief function theory. Experts provide exact and precise responses; hence, to calculate crowd workers' expertise, they measured their level of exactitude and precision of their responses in tasks. Crowd selection for spatial crowdsourcing was researched by [PERSON] et al. [32]. The authors proposed a framework to select the optimal subset of the crowd that best matches the crowdsourced task. Crowd workers' domain knowledge, performance history on previously accomplished work and location information are used for crowd worker selection. They have employed the Bayesian network for selecting and matching the crowd to suitable tasks. In addition, some studies have utilized recommendation-based methods [11] to select a suitable crowd. [PERSON] and [PERSON] [33] used collaborative filtering techniques to recommend a suitable crowd by inferring the crowd workers' level of expertise from their past activities. For group recommendation, [PERSON] et al. [26] have proposed a method called GRBMC to find the best group of workers to accomplish the crowdsourced task. The historical tasks are analyzed to infer the crowd's performance and characteristics to recommend a suitable group of crowd workers. For more complex tasks such crowdsourced testing, [PERSON] et al. [27] and [PERSON] et al.