Advances in Feature Selection for Data and Pattern Recognition: An Introduction

Stańczyk, Urszula; Zielosko, Beata; Jain, Lakhmi C.

doi:10.1007/978-3-319-67588-6_1

Cited by 18 publications

(13 citation statements)

References 25 publications

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“…Feature selection is one of the stage for preprocessing the data through the identification and selection of a subset of F features from the original data of D features (F < D) without any transformation [57]. In the domain of supervised learning, feature Selection attempts to maximize the accuracy of the classifier, minimizing the related measurement costs by reducing irrelevant and possibly redundant features [5,40,45,26,46,68,35,37,50,1]. Feature selection reduces the complexity and the associated computational cost and improves the probability that a solution will be comprehensible and realistic.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Feature Selection methods reduce the dimensionality of datasets by removing features that are considered as irrelevant or noisy for the learning task. This topic has received a lot of attention in machine learning and pattern recognition communities [4,46,68,35,37,50,1]. In any dataset, data can be seen as a collection of data points called instances.…”

Section: Introductionmentioning

confidence: 99%

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Feature and instance selection through discriminant analysis criteria

Dornaika

Moujahid²

2022

Soft Comput

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Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels.In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection.The first scheme is a wrapper technique, while the other two schemes are filtering techniques.In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-ofthe-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature and instance selection through discriminant analysis criteria

Dornaika

Moujahid²

2022

Soft Comput

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“…Various strategies can be applied to creating a committee, such as bootstrap resampling of the training data [40,41]. We decided to set up a committee of SVM classifiers trained using different hyperparameter values since the behaviour of the SVM strongly varies with its cost and bandwidth parameters, C and γ [42].…”

Section: Query By Committeementioning

confidence: 99%

Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

Wang

Brenning

2021

Remote Sensing

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Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature and Instance Selection Through Discriminant Analysis Criteria

Dornaika

Moujahid

2021

Preprint

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Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.

show abstract

Advances in Feature Selection for Data and Pattern Recognition: An Introduction

Cited by 18 publications

References 25 publications

Feature and instance selection through discriminant analysis criteria

Feature and instance selection through discriminant analysis criteria

Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

Feature and Instance Selection Through Discriminant Analysis Criteria

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