Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

Feature selection (FS) is mainly used as a pre-processing tool to reduce dimensionality by eliminating irrelevant or redundant features to be used for a machine learning or data mining algorithm. In this paper, we have introduced binary variant of a recently proposed meta-heuristic algorithm called Social Ski Driver (SSD) optimization. To the best of our knowledge, SSD has not been used yet in the domain of FS. Two binary variants of SSD are proposed using S-shaped and V-shaped transfer functions. Besides, the exploitation ability of SSD is improved by using a local search method, called Late Acceptance Hill Climbing (LAHC). The hybrid meta-heuristic is then converted to binary version by using said transfer functions. The proposed methods are applied on 18 standard UCI datasets and compared with 15 stateof-the-art FS methods. Also to check the robustness of the proposed method, we have applied it to 3 high dimensional microarray datasets and compared with 6 state-of-the-art methods. Achieved results confirm the superiority of the proposed methods compared to other meta-heuristic wrapper based FS methods considered here. Source code of this work is available at https://github.com/consigliere19/SSD-LAHC.INDEX TERMS Social ski driver optimization, feature selection, late acceptance hill climbing, UCI dataset, meta-heuristic optimization, microarray data.

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Influence maximization in a social network focuses on the task of extracting a small set of nodes from a network which can maximize the propagation in a cascade model. Though greedy methods produce good solutions to the aforementioned problem, their high computational complexity is a major drawback. Centrality‐based heuristic methods often fail to overcome local optima, thereby producing sub‐optimal results. To this end, in this article, a framework has been presented which involves community detection in a social network and the utilization of the Shuffled Frog Leaping algorithm, in maximizing the two‐hop spread of influence under the independent cascade model. Local search strategies like the Late acceptance based hill climbing have been employed to improve the solution further. Experiments performed on three real‐world datasets have shown that our method performs markedly well with respect to the comparing algorithms.

Stock market prediction is the process of determining the value of a company’s shares and other financial assets in the future. This paper proposes a new model where Altruistic Dragonfly Algorithm (ADA) is combined with Least Squares Support Vector Machine (LS-SVM) for stock market prediction. ADA is a meta-heuristic algorithm which optimizes the parameters of LS-SVM to avoid local minima and overfitting, resulting in better prediction performance. Experiments have been performed on 12 datasets and the obtained results are compared with other popular meta-heuristic algorithms. The results show that the proposed model provides a better predictive ability and demonstrate the effectiveness of ADA in optimizing the parameters of LS-SVM.

In today's data-driven world, every workforce is relentlessly exploiting the power of data to get that extra edge in order to triumph over the others. However, there is a saying that goes like, "Work smarter, not harder." Studies have shown that the amount of data people actually use is way smaller than the data being generated. This very fact gives rise to an important research topic, called dimension reduction, which is one of the smartest (not hardest) strategies for retrieving useful information (here, features) from a given high-dimensional dataset. Feature selection (FS) is among the best dimension reduction tactics available in the literature. To this end, in this paper, we have made an effort to introduce an FS framework called Py_FS that we have developed to simplify the task for the researchers. Py_FS currently provides an interesting combination of 12 wrapper-and 4 filter-based FS techniques along with various evaluation metrics. For converting the continuous search space to a binary search space, three transfer functions have been used. The algorithms have been experimented on two Microarray and four UCI datasets. To the best of our knowledge, it is the first ever framework to provide wrappers, filters, and evaluation metrics under one structure. The framework is highly flexible and can easily cater to the needs of the FS researchers. It is publicly hosted at the following link: Py_FS: A Python Framework for FS.

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