A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.</p

A bumble bees mating optimization algorithm for the feature selection problem

Summarization: The feature selection problem is an interesting and important topic which is relevant for a variety of database applications. This paper utilizes a relatively new bees inspired optimization algorithm, the bumble bees mating optimization algorithm, to implement a feature subset selection procedure while the nearest neighbor classification method is used for the classification task. Several metrics are used in the nearest neighbor classification method, such as the euclidean distance, the standardized euclidean distance, the mahalanobis distance, the city block metric, the cosine distance and the correlation distance, in order to identify the most significant metric for the nearest neighbor classifier. The performance of the proposed algorithm is tested using various benchmark data sets from the UCI machine learning repository. The algorithm is compared with two other bees inspired algorithms, the one is based on the foraging behavior of the bees, the discrete artificial bee colony, and the other is based on the mating behavior of the bees, the honey bees mating optimization algorithm. The algorithm is, also, compared with a particle swarm optimization algorithm, an ant colony optimization algorithm, a genetic algorithm and with a number of algorithms from the literature.Παρουσιάστηκε στο: International Journal of Machine Learning and Cybernetic

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications