Why don’t you use Evolutionary Algorithms in Big Data?

Stanovov, Vladimir; Brester, Christina; Kolehmainen, Mikko; Semenkina, O. E.

doi:10.1088/1757-899x/173/1/012020

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…All six classifiers have been used to evaluate the performance of dimensionality reduction, i.e., feature selection for five datasets and because of higher computational resource requirement, only naïve Bayes has been applied to the QSAR Oral Toxicity and Colon Cancer Dataset. Figures 6,7,8,9,10,11,12 show the comparative performance evaluation of classifiers using FS on each dataset in terms of accuracy, sensitivity, and specificity. Simulation results from Fig.…”

Section: Performance Evaluation Of Classifiers With Ccfsrfgmentioning

confidence: 99%

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Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.

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Section: Performance Evaluation Of Classifiers With Ccfsrfgmentioning

confidence: 99%

“…A wide range of search techniques can be applied to the FS process, for example, greedy search, best search, or evolutionary search [6]. Evolutionary algorithms (EA) are search techniques that are widely used for feature selection [7]. However, with the increase in data samples and features in a dataset, the search space also increases.…”

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confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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“…These algorithms are based on the concepts of natural selection and genetics [21]. Due to their flexibility, GAs are able to solve global optimization problems and optimize several criteria at the same time, like in our case the simultaneous selection of data re-balancing, feature engineering and prognostic algorithm techniques [22]. This is what makes them good candidates for our optimization problem.…”

Section: Optimization Metricsmentioning

confidence: 99%

The Impact of Metrics on the Choice of Prognostic Methodologies

Bieber

Wim

Santos

2022

AIAA AVIATION 2022 Forum

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Metrics play an important part in the development and application of prognostic methodologies as they provide the capability to characterize and assess the quality of remaining useful life predictions. Although there is a wide range of both, prognostic metrics and prognostic methodologies available, the choice of those often is a demanding and time consuming task. Additionally, they are often treated as two separate problems to solve, while the choice of metrics has an impact on the choice of prognostic methodology and vice versa. In this paper, we therefore present a framework with the capability to automatically choose prognostic settings given specific system data to account for five different prognostic metrics. We then apply this framework to an aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric has a big impact on the output of the generic prognostic framework and on the overall prognostic performance.

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“…Based on this literature review, there is no extensive research performed on these techniques using a range of datasets yet. In addition, studies from [16], [51], [52] show that evolutionary computations mostly use algorithms for complex and large optimization problems, such as the FS problem for big data. To address these issues, a CCEA-based FS approach is proposed in the next section.…”

Section: Number Of Objectivesmentioning

confidence: 99%

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

et al. 2020

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The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.

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Why don’t you use Evolutionary Algorithms in Big Data?

Cited by 14 publications

References 21 publications

Cooperative co-evolution for feature selection in Big Data with random feature grouping

Cooperative co-evolution for feature selection in Big Data with random feature grouping

The Impact of Metrics on the Choice of Prognostic Methodologies

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

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