Early Registration Deadline : 1 st May

Known as the GIGO (Garbage In, Garbage Out) principle, the quality of the input data highly influences or even determines the quality of the output of any machine learning, big data and data mining algorithm. The input data which is often represented by a set of features may suffer from many issues. Feature manipulation is an effective means to improve the feature set quality, but it is a challenging task. Evolutionary computation (EC) techniques have shown advantages and achieved good performance in feature manipulation. This paper reviews recent advances on EC based feature manipulation methods in classifcation, clustering, regression, incomplete data, and image analysis, to provide the community the state-of-the-art work in the field. INTRODUCTION Machine learning and big data/data mining methods have shown great success in many real-world applications. Their performance highly depends on the quality of the input data, which is represented by a set of features describing different properties of a problem [52]. However, the feature set is often not of adequate quality. The common issues are high-dimensionality (i.e. ”the curse of dimensionality”) and containing redundant features, useless features or even noisy features. They often lead to poor performance, i.e. low accuracy, long processing time, over complex and incomprehensible models [24]. Feature manipulation, including feature selection and feature construction or extraction, can improve the performance [50], where feature selection is to select a subset of useful (relevant) features from originals while feature construction or extraction is to create new high-level informative features. Evolutionary computation (EC) techniques have powerful search ability, do not make any assumptions and do not require domain knowledge, which make them promising in feature manipulation [9]. EC has been increasingly popular in feature manipulation in recent years [51]. The most widely used methods are genetic algorithms (GAs), genetic programming (GP), particle swarm optimisation (PSO), and ant colony optimisation (ACO). Most evolutionary feature manipulation work is on feature selection in classification, although GP has been used for feature construction or extraction. There are a number of papers reviewing feature manipulation for data mining, mainly on classification and clustering [2, 24– 26, 47, 50, 51]. There exist only two surveys on evolutionary feature manipulation,[8] in 2013 and [51] in 2016, and both focus on feature selection in classification. However, evolutionary feature manipulation has been successfully applied to clustering, regression, incomplete data, and image analysis. This article aims to give an overview of evolutionary feature manipulation in the above areas. FEATURE MANIPULATION Feature manipulation is a challenging task, where the two main reasons are the large search space and the interactions between features. In feature selection, the size of the search space is 2n if there are n features available in the dataset. It is even larger in feature construction or extraction since both tasks involve operators in addition to the features. There are often interactions among features, including both positive interactions and negatives interactions. Feature manipulation needs to find complementary features and utilise the positive interactions in order to improve the learning performance. Figure 1 shows the overall system of feature manipulation. Note that for supervised learning tasks, such as classification or regression, the feature manipulation procedure should be performed on the training set only, not the whole dataset. Otherwise, there will be a feature manipulation bias issue [17]. The detailed feature manipulation procedure is shown in the orange colour box in the figure. The key steps are the feature set discovery and the feature set evaluation, where a powerful search technique and a good performance evaluation measure are needed, respectively. There are many different ways to evaluate a feature set. Depending on whether a learning algorithm is involved, feature manipulation methods can be grouped into filter, wrapper, and embedded methods [24, 25], where filters do not Evolutionary Feature Manipulation in Data Mining/Big Data