# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 1 Introduction  
A considerable amount of research in the field of machine learning (ML) is concerned with developing methods that automate classification tasks [1]. Classification tasks are involved in several real-world applications, in such fields as civil engineering [2, 3], medicine [4], land use [5], energy [6], investment [7], and marketing [8]. It is obvious that problems in the engineering domain are multi-class issues. Hence, there is a need to establish a learning framework for solving multi-level classification problems efficiently and effectively, which is the primary purpose of this study. Various classification approaches have been proposed and used to solve real-life problems, ranging from statistical methods to ML techniques, such as linear classification (NaiveBayes classifier and logistic regression), distance estimation (k-nearest neighbors), support vector machines (SVM), rule and decision-tree-based methods, and neural networks, to name a few [9]. Some studies have used fuzzy synthetic evaluation to classify seismic damage and assess risks to mountain tunnels [10], while others have used artificial neural networks (ANNs), SVM, Bayesian networks (Bayes Net) and classification trees (C5.0) to classify information that bears on project disputes and possible resolutions [11]. Nevertheless, many studies have also demonstrated that machine learning methods cannot solve multi-level classification problems efficiently or do not yield suitable forecasts for practical applications [12; 13; 14; 15]. For example, the k-nearest neighbors (KNN) method is a lazy learner and very slow; a decision tree (DT) is good for classification problems but becomes complex to interpret if the tree grows largely, leading to overfitting. Multi-level or multi-class classification problems are typically more difficult to solve than binary-class problems because the decision boundary in a multi-class classification problem tends to be more complex than that in a binary classification problem [16]. Therefore, it is preferable to break down a multi-class problem into several two-class problems and combine the output of these binary classifiers to obtain the final, multi-class decision [17]. Decomposition strategies [13] are commonly used to solve classification problems with multiple classes. These methods transform a multi-class classification problem into several binary classification problems [16]. Thus, many machine learning methods were applied with decomposition strategies, such as one-against-rest [18] and one-against-one [19], to improve the results. One-against-one (OAO) and one-against-rest (OAR) are the most widely used decomposition strategies. The literature [19; 20; 21] compares some OAO and OAR classifiers that are based on single classification algorithms, including ANN, DT, KNN, linear discriminant analysis (LDA), logistic regression (LR), and SVM, and indicates that single classification algorithms combined with the OAO approach usually outperform those combined with the OAR approach. Studies of binary classification regard the SVM as one of the most effective machine learning algorithms for classification [22; 23]. The SVM is an algorithm with the potential to support increasingly efficient methods for multi-class classification. In particular, the OAO strategy has been used with very well-known software tools to model multi-class problems for SVM. For the SVM, the OAO method generally outperforms the OAR and other SVM-based multi-class classification algorithms [16; 24; 25]. Therefore, integrating OAO with the SVM yields a method (OAO-SVM) that is potentially effective for solving multi-class classification problems. However, one of main challenges for the classical SVM is its high computational complexity, because the algorithm itself involves constrained optimization programming. The least squares support vector machine (LSSVM) is a highly enhanced machine-learning technique with many advanced features that support generalization and fast computation [26].