Summary
Efficient management of big data becomes challenging in recent decades. Online Feature Selection (OFS) is one type of online learning in contrast to batch learning, allowing a classifier to have small and fixed number of features. The major aim of this work is to introduce an OFS algorithm supported on meta‐heuristic algorithm that exploits the MapReduce paradigm. A novel Hybrid Multi‐Objective Firefly and Simulated Annealing (HMOFSA) algorithm is proposed to select optimal set of features. Therefore, as a first step, the original big dataset is decomposed into blocks of examples in the map phase. Subsequently, HMOFSA algorithm is employed to choose the selected features from examples. After that, the attained partial outcomes will be combined into a final vector of features in the reduce phase and evaluated using Kernel Support Vector Machine (KSVM) classifier. The mentioned OFS approach is analyzed with the help of the well‐known classifiers (Logistic Regression, KSVM and Naïve Bayes) developed within the Spark framework. Experiments were conducted on big datasets, containing 66 million samples and 2000 attributes that confirm the proficiency of proposed work. The proposed KSVM classifier results are measured in terms of the metrics like Precision, Recall, Geometric‐mean (G‐mean), F‐measure, and accuracy.
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
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