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The "curse of dimensionality" issue caused by high-dimensional datasets not only imposes high memory and computational costs but also deteriorates the capability of learning methods. The main purpose of feature selection is to reduce the dimensionality of these datasets by discarding redundant and irrelevant features, which improves the performance of the learning algorithm. In this paper, a new feature selection algorithm, referred to as FSBACOM, was presented based on the binary ant system (BAS). The proposed method sought to improve feature selection by decreasing redundancy and achieved an optimum solution by increasing search space in a short time. For this purpose, the features were organized sequentially in a circular graph, where each feature was connected to the next one with two select/deselect edges. The proposed representation of the search space reduced computational time significantly, particularly on the high-dimensional datasets. Inspired from genetic algorithm and simulated annealing, a damped mutation strategy was introduced to avoid falling into local optima. In addition, a new idea was utilized to reduce the redundancy between selected features as far as possible. The performance of the proposed algorithm was compared to that of state-of-the-art feature selection algorithms using different classifiers on real-world datasets. The experimental results confirmed that FSBACOM significantly reduces computational time and achieves better performance than other feature selection methods. [ABSTRACT FROM AUTHOR]

• Some ELMs are used in parallel as experts to map data into discriminative space. • The training data are easily grouped into some clusters in the new feature space. • An index is calculated per group to help finding corresponding groups of a sample. • A tree is used to find corresponding groups of a sample and applying kNN on it. • The method is evaluated on 4 big datasets in absence and presence of noise. kNN algorithm, as an effective data mining technique, is always attended for supervised classification. On the other hand, the previously proposed kNN finding methods cannot be considered as efficient methods for dealing with big data. As there is daily generated and expanded big datasets on different online and offline servers, the efficient methods for such data must be introduced to find kNN. Moreover, massive amounts of data contain more noise and imperfection data samples that significantly increase the need for a robust kNN finding method. In this paper, a new fast and robust kNN finding framework is introduced to deal with the big datasets. In this method, a group of most relevant data samples to an input data sample are detected and the original kNN method is applied on them for finding the final nearest neighbors. The main goal of this method is dealing with the big datasets in an accurate, fast, and robust manner. Here, the training data samples of each label are grouped into some partitions based on the output of some mini-classifiers (i.e. ELM classifier). In fact, the behavior of the mini-classifiers is the basis of partitioning the training data samples. These mini-classifiers are trained using non-overlapping subsets of the training set in the form of each mini-classifier a subset. Here, an index is calculated for each partition to make the corresponding partition finding faster using a tree structure in which each partition index is fallen into a leaf. Then, outputs of the mini-classifiers for an input test sample are used to find the corresponding group of most relevant data samples to the input data sample on the tree. Experimental results indicate that the proposed method has better performance in most cases and comparable performance on other cases of original and noisy big data problems. [ABSTRACT FROM AUTHOR]