A two-layer feature selection method using Genetic Algorithm and Elastic Net

Amini, Fatemeh; Hu, Guiping

doi:10.1016/j.eswa.2020.114072

Cited by 122 publications

(58 citation statements)

References 29 publications

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“…The procedure of GA consists of the following four steps 1 : Individual encoding : Each individual is encoded as binary vector of size P , where the entry b i = 1 states for the predictor p i that is defined for that individual, b i = 0 if the predictor p i is not included in that particular individual ( i = 1,…, P ). Initial population : Given the binary representation of the individuals, the population is a binary matrix where its rows are the randomly selected individuals, and the columns are the available predictors. An initial population with a predefined number of individuals is generated with a random selection of 0 and 1 for each entry. Fitness function : the fitness value of the individual in the population is calculated using predefined fitness function.…”

Section: Supplementary Methodsmentioning

confidence: 99%

“…The genetic operators are, Selection (randomly selection of members based on their fitness value; fitter members are more likely to be chosen), Crossover (the new generation is created by exchanging elements between two selected parents from the previous step), Mutation (elements in a selected member is changed), and Stop Criteria (the criteria and indicate the end of the search) 1 . In our study we used roulette wheel selection for selection of the possible valuable solutions to producing offsprings for the next generation.…”

Section: Supplementary Methodsmentioning

confidence: 99%

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Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Zamani

Sadr

Javadi

2021

Preprint

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Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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Section: Supplementary Methodsmentioning

confidence: 99%

Section: Supplementary Methodsmentioning

confidence: 99%

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Zamani

Sadr

Javadi

2021

Preprint

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“…In [68], a k-Nearest-Neighbors technique, for which a genetic algorithm is utilized for the efficient feature selection to decrease the dataset dimensions and improve the classification accuracy, is employed for diagnosing the stage of patients' disease. Moreover, in [69] a new two-layer feature selection approach that combines a wrapper and an embedded method in constructing an appropriate subset of predictors is proposed. In the first layer of this technique, the Genetic Algorithm has been adopted as a wrapper to search for the optimal subset of predictors, which aims to reduce the number of predictors and the prediction error.…”

Section: Related Workmentioning

confidence: 99%

A novel community detection based genetic algorithm for feature selection

2021

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The feature selection is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. Comparing the performance of the proposed method with three new feature selection methods based on PSO, ACO, and ABC algorithms on three classifiers showed that the accuracy of the proposed method is on average 0.52% higher than the PSO, 1.20% higher than ACO, and 1.57 higher than the ABC algorithm.

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“…The ABO applying the lean metaheuristic design concept was designed to be fast in obtaining results, avoid stagnation, use few parameters, and be efficient and effective; hence, it is the choice for this comparative study. It was actually designed to complement the existing algorithms such as the Genetic Algorithm [ 52 ], Simulated Annealing [ 53 ], Ant Colony Optimization [ 54 ], and Particle Swarm Optimizations [ 55 ]. Using these vocalizations, the African buffalos organize themselves in their navigation through the African forests in search of lush green pastures to satisfy their huge appetite [ 35 ].…”

Section: The Comparative Algorithmsmentioning

confidence: 99%

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Odili¹,

Ahmad

Zarina

2021

Computational Intelligence and Neuroscience

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This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

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A two-layer feature selection method using Genetic Algorithm and Elastic Net

Cited by 122 publications

References 29 publications

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

A novel community detection based genetic algorithm for feature selection

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

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