## 1 Introduction  
Antimicrobial peptides (AMPs) are part of innate immunity and are natural antibiotics encoded by specific genes [1]. They are produced by various tissues and cell types of human, plant and animal species. These antimicrobial peptides usually contain 12 to 50 amino acids [2]. Nowadays, in parallel with the elevated use of antibiotics, resistance to antibiotics is rapidly increasing. The World Health Organization (WHO) reported that antimicrobial resistance continues to rise up all over the world and new resistance mechanisms emerge. Therefore, we could be faced with an era when infections can no longer be treated with antibiotics [3]. The increasing number of bacteria, which are resistant to antibiotics, create a need for the development of new antimicrobial agents that can be applied in treatment [4]. Studying the properties of antimicrobial peptides in detail is a very important topic for drug design [5]. Although AMPs are mainly used to kill Gram-positive and Gram-negative bacteria, they have potential to fight against mycobacteria, viruses, and cancerous cells. In this respect, AMPs are considered as a powerful alternative to antibiotics since they have lower risk to develop resistance [3,4]. Hence, discovering or designing novel antimicrobial peptides became a major field of interest. The increasing interest in AMPs has recently increased the efforts to discover new peptides with antimicrobial activities. Prior to the time-consuming, costly, and difficult production processes, the accurate prediction of the activity of candidate peptides is very important. Along this line, several computational approaches such as de novo computational design [6, 7, 8, 9], linguistic model [10, 11], pattern insertion algorithm [12, 13, 14, 15], and evolutionary-genetic algorithms [16, 17, 18, 19] have been proposed for predicting the antimicrobial activity of AMPs and for identifying promising AMP candidates without undertaking expensive wet-lab experiments. Among different computational methods for the estimation of antimicrobial peptides [20], the use of machine learning methods became popular [21, 22, 23, 24]. Machine learning is a computational technique where the generated models can make predictions via learning data [25]. Significant advancements in computational power and easy-to-use statistical learning tools that have come to the fore in recent years have increased the popularity of machine learning approaches. In this respect, machine learning which can leverage large datasets that are produced by high-throughput methods has become a viable option for the accurate classification of AMPs [26]. [PERSON] et al. used the Support Vector Machine (SVM) method for prediction and classification of peptides on data which were collected from the Antimicrobial Peptides Database [24]. Their model is based on amino acid composition, and by using five-fold cross-validation, they obtained 92.14% accuracy [24]. [PERSON] et al. attempted to identify essential AMP potential regions via applying Random Forest (RF) as a classification algorithm [27]. [PERSON] et al. made predictions for antimicrobial peptides on different organisms including amphibians, humans, fish, insects, plants, bacteria, and mammals [28]. Amino acid (aa) compositions, amino acid pairs, and the physico-chemical properties are used as features. They performed feature selection, and applied RF, SVM, k-Nearest Neighbor (kNN) algorithms. They reported that RF generated the best result, which was over 92% accuracy on all tested organisms [28]. [PERSON] et al. also utilized an RF algorithm for AMP prediction using physico-chemical properties as features [23]. They grouped each property into three specific classes. For example, for hydrophobicity property, three classes are polar, neutral, and hydrophobic, while these three classes are positive, neutral, and negative for net charge property.