## 1 Introduction  
The construction industry is a key sector of the national economy for countries all around the world [1]. Since construction is a labor-intensive industry, poor construction labor productivity (CLP) usually causes cost and time overruns in projects [2; 3]. To overcome this issue, the construction industry is constantly trying to identify CLP improvement strategies [4]. However, project managers first require a CLP model that helps them identify which factors lead to positively changing CLP and by how much [5; 6]. Furthermore, the accurate prediction of CLP is essential for effective scheduling and planning prior to and during project execution [7]. CLP is a form of efficiency measure that is mainly defined as a ratio of units of output (i.e., project components) to units of input (e.g., labor work hours, and labor cost) or vice versa [7; 8; 9]. Many factors can potentially affect CLP, reducing the accuracy of a predictive model and imposing the risk of data overfitting [10]. The power of any prediction method relies on choosing the proper factors that affect the model output [11]. Since the identification of factors that influence construction productivity is essential for productivity performance improvement, different studies identified numerous factors that affect CLP [12]. Several studies used questionnaire surveys to identify factors with the greatest influence on CLP [13; 14; 15; 16; 17; 18; 19]. However, many studies notably focused on finding CLP factors, and few works are found in the literature on labor productivity prediction [20]. The most reliable estimate of productivity can be achieved using past project data because the important predictive productivity information can be extracted for future project management and planning [12]. Studies on productivity prediction can be classified into three types: statistical, simulation, and artificial intelligence (AI) techniques. Regression analysis is one of the most common statistical techniques [21]. [PERSON] and [PERSON] [22] presented multiple regression models to quantify the effect of factors influencing masonry labor productivity. [PERSON] and [PERSON] [23] presented a multiple linear regression model as a statistical method for output prediction to evaluate the impact of 10 factor groups on CLP. Nevertheless, regression models are limited by the number of factors that can be considered as inputs [24]. System dynamics is one of the most applicable simulation techniques and is able to model a dynamic system [25]. Although system dynamics models are able to capture the probabilistic uncertainties of real-world systems, they cannot capture non-probabilistic uncertainties, that is, subjective or linguistically expressed information [26]. On the other hand, AI techniques, such as artificial neural network (ANN), are able to learn from experience to enhance their performance, adapt themselves to changes, and find patterns among datasets, which makes them effective prediction methods [27]. Accordingly, several AI techniques have been successfully applied to modeling and predicting construction productivity, which is discussed in the literature review section. CLP is affected by numerous factors that reduce the accuracy of the predictive model and impose the risk of data overfitting [10]. Feature selection (FS) is one of the important preprocessing procedures for data mining. Therefore, it is necessary to apply effective FS methods that are able to select key features affecting CLP and reject the nonessential features in order to achieve high prediction accuracy and reduce model complexity [28; 29]. Filter and wrapper methods are two types of FS. Filter methods offer less computational time and work without a learning algorithm. However, they cannot consider model prediction, and their classification accuracy is lower, compared to wrapper methods [30]. Wrapper methods use a learning algorithm to evaluate the set of most suitable features. However, their applications are limited by high computational complexity.