Feature selection considering the composition of feature relevancy

Gao, Wanfu; Hu, Liang; Zhang, Ping; He, Jialong

doi:10.1016/j.patrec.2018.06.005

Cited by 82 publications

(32 citation statements)

References 12 publications

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“…In this section, some commonly used feature selection approaches such as a new feature selection (NFS) [74], the unsupervised feature selection (UFS) [75] and mutual information maximization (DRJMIM) [76] are utilized to validate the effectiveness of the proposed method. The fig.10 show the average number of selected by novel feature selection methods.…”

Section: Comparison With the Novel Feature Selection Methodsmentioning

confidence: 99%

A New Hybrid Seagull Optimization Algorithm for Feature Selection

2019

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Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

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Section: Comparison With the Novel Feature Selection Methodsmentioning

confidence: 99%

A New Hybrid Seagull Optimization Algorithm for Feature Selection

2019

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“…In traditional single-label feature selection methods, the average classification accuracy is usually used to evaluate the already-selected feature subset [2], [4], [10]- [13]. However, the evaluation metrics in multi-label feature selection methods are much more complicated than single-label feature selection methods.…”

Section: B Multi-label Evaluation Metricsmentioning

confidence: 99%

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

Gao

et al. 2020

IEEE Access

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Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image annotation, etc. However, traditional multi-label feature selection methods employ cumulative summation strategy to design methods, which suffers from the problem of overestimation the redundancy of some candidate features. Additionally, the cumulative summation strategy will lead to the high value of the goal function when one candidate feature is completed related with one or few already-selected features, but it is almost independent from the majority of alreadyselected features. To address these issues, we propose a new multi-label feature selection method named Feature Redundancy Maximization (FRM), which combines the cumulative summation of conditional mutual information with the 'maximum of the minimum' criterion. Additionally, FRM can be rewritten as another form of multi-label feature selection method that employs interaction information as a measure of feature redundancy that obtains an accurate score of feature redundancy as the number of already-selected features increases. Finally, extensive experiments are implemented on fourteen benchmark multi-label data sets in comparison to six state-of-the-art methods. The experimental results demonstrate the superiority of the proposed method.

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“…The filter approach is computationally efficient but usually yields lower prediction accuracy than the wrapper approach. Recent studies on this approach have focused on maximizing variable relevancy while minimizing variable redundancy based on information theory [28][29][30]. The wrapper approach evaluates variable subsets by building prediction models directly on the subsets using a learning algorithm [31].…”

Section: Related Workmentioning

confidence: 99%

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

Kim

Kang

2019

Energies

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Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the performance of prediction models. Typically, prediction models learned by different learning algorithms exhibit different sensitivities with regard to irrelevant variables. Algorithms with low sensitivities are preferred as a first trial for building prediction models, whereas a variable selection procedure is necessarily considered for highly sensitive algorithms. In this study, we investigate the effect of irrelevant variables on three well-known representative learning algorithms that can be applied to both classification and regression tasks: artificial neural network, decision tree (DT), and k-nearest neighbors (k-NN). We analyze the characteristics of these learning algorithms in the presence of irrelevant variables with different model complexity settings. An empirical analysis is performed using real-world datasets collected from a semiconductor manufacturer to examine how the number of irrelevant variables affects the behavior of prediction models trained with different learning algorithms and model complexity settings. The results indicate that the prediction accuracy of k-NN is highly degraded, whereas DT demonstrates the highest robustness in the presence of many irrelevant variables. In addition, a higher model complexity of learning algorithms leads to a higher sensitivity to irrelevant variables.

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Feature selection considering the composition of feature relevancy

Cited by 82 publications

References 12 publications

A New Hybrid Seagull Optimization Algorithm for Feature Selection

A New Hybrid Seagull Optimization Algorithm for Feature Selection

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

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