# Application of Machine Learning Methods for Pallet Loading Problem
## 3 Methodology
### Machine Learning Methods for Classification  
Classification is a technique employed in AI and ML that is used to label a given dataset into different classes in order to extract useful information [39]. Currently, classification methods are used in many fields, such as speech recognition [40], image classification [41], and document classification [42]. Fundamentally, there are two classification methods, which are binary and multi-class classifications [43]. In the binary classification, a given problem is divided into two situations or classes, such as male/female in gender prediction [44] and sick/not sick in disease prediction [45]. On the other hand, multi-class classification is used in problems in which there are more than two cases, such as estimating different flower types [46]. There are classification methods such as ANNs, Support Vector Machines (SVMs), KNN, Naive Bayes (NB), Decision Trees (DT), and Random Forest. In this study, all these classification methods are used because of their success and frequent use in recent research studies. In the process of the development of ANNs, researchers were inspired by the human nervous system [47]. These ANNs consist of input, output, and hidden layers, and they are trained and tested on a real dataset to classify the data into different classes. Later on, the trained ANN models try to predict the outcome of any new data for further analysis [48]. This whole process is governed by the neurons in the layers and an activation function. This activation function (also called the objective function) is a predefined goal for a particular ANN model. The SVM enables the use of vectors (separators) to divide the training data into areas (categories) as far away as possible [49]. It assigns the test data a certain category according to the area of a particular vector that they fall into and uses a subset of training points when making decisions in the classification process. For this reason, the SVM process uses memory efficiently and is effective for high-dimensional data. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Storage Time (_Fr_)** & **Humidity (_Fin_)** & **Pallet Surface Gapped (_F\({}_{G}\)_)** & **Interlock (_F\({}_{I}\)_)** & **Perpendicular Dimension to Base (_F\({}_{o}\)_)** \\ \hline
0 & 1 & 0–0.45 & 1.1 & Yes & 0.92 & Yes & 0.60 & 1 st Shortest Dimension of Box & 1 \\ \hline
1–3 & 0.7 & 0.45–0.55 & 1 & No & 1 & No & 1 & 2 nd Shortest Dimension of Box & 0.9 \\ \hline
4–10 & 0.65 & 0.55–0.65 & 0.9 & & & & & Longest Dimension of Box & 0.8 \\ \hline
11–30 & 0.6 & 0.65–0.75 & 0.8 & & & & & & \\ \hline
31–90 & 0.55 & 0.75–0.85 & 0.7 & & & & & & \\ \hline
91–120 & 0.5 & 0.85–1 & 0.5 & & & & & & \\ \hline
121–300 & 0.45 & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Dynamic strength factors [38]. The KNN method classifies an object in the input parameter field with the majority vote of the object's neighbors [50]. In this way, an object is assigned to the most common class among its nearest neighbor, i.e., k (user-specified integer). This method classifies objects according to their similarity in the point of property. It is a simple method to implement and provides effective results in large test datasets [51]. The DT method is a flowchart-like tree structure in which an inner node represents the property (or attribute), a branch represents a decision rule, and each leaf node represents the result [52]. The top node in a DT is termed the root node. It learns to partition according to the attribute value and sections the tree recursively with the recursive partitioning call. This flowchart-like structure in a DT helps in decision-making [53]. Therefore, DTs are easy to understand and interpret. The Random Forest method is a supervised learning algorithm. It can be used for both classification and regression. It is also the most flexible and easy-to-use algorithm [54].