With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection.

Feature selection techniques in the context of big data: taxonomy and analysis

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Dynamic Architecture Based Deep Learning Approach for Glioblastoma Brain Tumor Survival Prediction

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms.

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An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

Breast cancer is one of the significant tumor death in women Computer-aided diagnosis (CAD) supports the radiologists in recognizing the irregularities in an efficient manner In this work, a novel CAD system proposed for mammogram image analysis based on grey wolf optimizer (GWO) with rough set theory Texture, intensity, and shape-based features are extracted from mass segmented mammogram images To derive the appropriate features from the extracted feature set, a novel dimensionality reduction algorithm is proposed based on GWO with rough set theory GWO is a novel bio-inspired optimization algorithm, stimulated based on hunting activities and social hierarchy of the grey wolves In this paper, a hybridization of GWO and Rough Set (GWORS) methods are used to find the significant features from the extracted mammogram images To evaluate the effectiveness of the proposed GWORS, we compare it with other well-known rough set and bio-inspired feature selection algorithms including particle swarm optimize, genetic algorithm, Quick Reduct and Relative Reduct From empirical results, it is observed that the proposed GWORS outperforms the other techniques in terms of accuracy, F-Measures and receiver operating characteristic curve

A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Breast cancer is the second most common cancers discovered around the world and that record for one-fourth of all cancers in women. Among the other kinds of diseases, breast cancer causes more number of deaths in many countries. An early identification for breast tumor gives the opportunity of its cure; therefore, an extensive amount of investigations are presently setting on to recognize techniques that could identify breast cancer in its initial phases. The healthcare sector has a tremendous amount of information and imperative data about patients and their well-being conditions. Hence, it is the need of the hour to utilize that huge information for medical practitioners to predict the disease. One approach for taking care of this issue has been handled by numerous researchers utilizing Machine Learning (ML) strategies to upgrade the prediction procedure through applying different tree-based classifiers. However, most of the tree based ML algorithms will not be able to handle huge amount of complex data. This issue is addressed through efficient tree-based classifiers (Decision Tree, Random Forest classifier, gradient boosting classifier) with Apache Spark framework. The experiments are conducted using Wisconsin Breast Cancer Dataset (WBCD) from UCI repository. Experimental results have demonstrated that the Random Forest Classifier outperformed the other two tree-based classification algorithms in most of the cases form this research study.

V. Yuvarajan

Papers

A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Spark Based Framework for Breast Cancer Analysis