Feature selection (FS) is an important research topic in the area of data mining and machine learning. FS aims at dealing with high dimensionality problem. It process selecting relevant features removing irrelevant, redundant noisy ones, intending to obtain best performing subset original without any transformation. This paper provides a comprehensive review literature supplement insights recom...

In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus allowing to discard several variables in one step...

Feature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for recently introduced distance-based model is considered regression problems. The study contextualized by first providing an umbrella review (review reviews) recent development in research field. We then propose a saliency-based one-shot wrapper a...

Kernel fisher discriminant analysis (KFD) is a successful approach to classification. It is well known that the key challenge in KFD lies in the selection of free parameters such as kernel parameters and regularization parameters. Here we focus on the feature-scaling kernel where each feature individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune th...

In the big data era, the sizes of datasets have increased dramatically in terms of the number of samples, features, and classes. In particular, there exists usually a hierarchical structure among the classes. This kind of task is called hierarchical classification. Various algorithms have been developed to select informative features for flat classification. However, these algorithms ignore the...

In this paper we propose the general scheme of defining hybrid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that “hybridization” has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifi...

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

Feature selection (FS) is a process in which the most informative and descriptive characteristics of a signal that will lead to better classification are chosen. The process is utilized in many areas, such as machine learning, pattern recognition and signal processing. FS reduces the dimensionality of a signal and preserves the most informative features for further processing. A speech signal c...

Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. This paper presents a novel feature selection algorithm based on Bacteria Foraging Optimization (BFO). The algorithm is applied to coefficients extracted by discrete cosine transforms (DCT...