A modified whale optimization algorithm for enhancing the features selection process in machine learning

In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. Whale Optimization Algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored and not introduced into machine learning models. This paper proposes a new and improved version of the whale algorithm entitled Modified Whale Optimization Algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine, naïve Bayes model. To test this new approach and the performance, the breast cancer datasets were used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models

Adaptive Learning Based Whale Optimization and Convolutional Neural Network Algorithm for Distributed Denial of Service Attack Detection in Software Defined Network Environment

SDNs (Software Defined Networks) have emerged as a game-changing network concept. It can fulfill the ever-increasing needs of future networks and is increasingly being employed in data centres and operator networks. It does, however, confront certain fundamental security concerns, such as DDoS (Distributed Denial of Service) assaults. To address the aforementioned concerns, the ALWO+CNN method, which combines ALWOs (Adaptive Learning based Whale Optimizations) with CNNs (Convolution Neural Networks), is suggested in this paper. Initially, preprocessing is performed using the KMC (K-Means Clustering) algorithm, which is used to significantly reduce noise data. The preprocessed data is then used in the feature selection process, which is carried out by ALWOs. Its purpose is to pick out important and superfluous characteristics from the dataset. It enhances DDoS classification accuracy by using the best algorithms.  The selected characteristics are then used in the classification step, where CNNs are used to identify and categorize DDoS assaults efficiently. Finally, the ALWO+CNN algorithm is used to leverage the rate and asymmetry properties of the flows in order to detect suspicious flows specified by the detection trigger mechanism. The controller will next take the necessary steps to defend against DDoS assaults. The ALWO+CNN algorithm greatly improves detection accuracy and efficiency, as well as preventing DDoS assaults on SDNs. Based on the experimental results, it was determined that the suggested ALWO+CNN method outperforms current algorithms in terms of better accuracies, precisions, recalls, f-measures, and computational complexities

Variable selection in gamma regression model using chaotic firefly algorithm with application in chemometrics

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Regression modeling has received much attention in several science fields. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, chaotic firefly algorithm is proposed to perform variable selection for gamma regression model.  A real data application related to the chemometrics is conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellingerant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew in sensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC scorethan ATM

Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets

Binary Black Widow Optimization Algorithm for Feature Selection Problems

This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. Recently, a new metaheuristic algorithm, known as the Black Widow Optimization (BWO) algorithm, had great results when applied to a range of daunting design problems in the field of engineering, and has not yet been applied to FS problems. In this thesis, we are proposing a modified Binary Black Widow Optimization (BBWO) algorithm to solve FS problems. The FS evaluation method used in this study is the wrapper method, designed to keep a degree of balance between two significant processes: (i) minimize the number of selected features (ii) maintain a high level of accuracy. To achieve this, we have used the k-nearest-neighbor (KNN) machine learning algorithm in the learning stage intending to evaluate the accuracy of the solutions generated by the (BBWO). The proposed method is applied to twenty-eight public datasets provided by UCI. The results are then compared with up-to-date FS algorithms. Our results show that the BBWO works as good as, or even better in some cases, when compared to those FS algorithms. However, the results also show that the BBWO faces the problem of slow convergence due to the use of a population of solutions and the lack of local exploitation. To further improve the exploitation process and enhance the BBWO’s performance, we are proposing an improvement to the BBWO algorithm by combining it with a local metaheuristic algorithm based on the hill-climbing algorithm (HCA). This improvement method (IBBWO) is also tested on the twenty-eight datasets provided by UCI and the results are then compared with the basic BBWO and the up-to-date FS algorithms. Results show that the (IBBWO) produces better results in most cases when compared to basic BBWO. The results also show that IBBWO outperforms the most known FS algorithms in many cases

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity