Feature Selection for High-Dimensional Data

Boln-Canedo, Vernica; Snchez-Maroo, Noelia; Alonso‐Betanzos, Amparo

doi:10.1007/978-3-319-21858-8

Cited by 184 publications

(121 citation statements)

References 69 publications

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“…In this section, we evaluate our approach using artificial datasets 3 in which the optimal feature subset is known in advance. The total number of features, the number of instances and the relevant features of each selected dataset are given in table 7 .…”

Section: Validation Using Artificial Datasetsmentioning

confidence: 99%

“…On CA100, GAB-BBO gives the best accuracy of 93.02% with index of success of 74% which is less than the one obtained by BF/BS. This is due to the fact that in CA100 dataset there are some irrelevant features that are informative to the classifier 3 . On M1, the proposed algorithm gives accuracy of 100% and selects all the relevant features.…”

Section: Validation Using Artificial Datasetsmentioning

confidence: 99%

“…FS is the process of removing irrelevant and redundant features from the original ones. In contrast to FE, FS maintains the original features which is more useful for applications needing model interpreting and knowledge extraction 3 . In intrusion detection, the semantic of the features is important for subsequent diagnosis of the cause of the attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Feature selection problem 3 is an active area of research in machine learning that has attracted a lot of attention. It is the process of selecting a subset of d relevant, non-redundant features from D original ones (d < D).…”

Section: Introductionmentioning

confidence: 99%

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GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd'99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

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Section: Validation Using Artificial Datasetsmentioning

confidence: 99%

Section: Validation Using Artificial Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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“…Hence, no feature selection method that terminates in a reasonable amount of time performs optimally in all situations (Kohavi and John, 1997;Bolón-Canedo et al, 2015). It is, therefore, not surprising that an immense and ever growing number of different heuristic strategies exist (Bolón-Canedo et al, 2015;Stańczyk and Jain, 2015), so many in fact that it makes choosing one of them difficult since it requires a relatively deep understanding not only of the mechanism of available feature selection methods, but also and in particular, the functionality of the underlying classifier.…”

Section: Previous Workmentioning

confidence: 99%

Variable Selection for Road Segmentation in Aerial Images

Warnke

Bulatov

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:For extraction of road pixels from combined image and elevation data, Wegner et al. (2015) proposed classification of superpixels into road and non-road, after which a refinement of the classification results using minimum cost paths and non-local optimization methods took place. We believed that the variable set used for classification was to a certain extent suboptimal, because many variables were redundant while several features known as useful in Photogrammetry and Remote Sensing are missed. This motivated us to implement a variable selection approach which builds a model for classification using portions of training data and subsets of features, evaluates this model, updates the feature set, and terminates when a stopping criterion is satisfied. The choice of classifier is flexible; however, we tested the approach with Logistic Regression and Random Forests, and taylored the evaluation module to the chosen classifier. To guarantee a fair comparison, we kept the segment-based approach and most of the variables from the related work, but we extended them by additional, mostly higher-level features. Applying these superior features, removing the redundant ones, as well as using more accurately acquired 3D data allowed to keep stable or even to reduce the misclassification error in a challenging dataset.

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Medical Expert Systems

Kerkhof

Alonso‐Betanzos

Moret‐Bonillo

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Medical informatics has undergone a significant transformation in the past two decades. Software that once was only running on main frames, is nowadays available for anyone on any desired location via tablets and smartphones. Interestingly, the original ambitious endeavors to construct systems that ultimately would encompass the whole of medicine have not been realized thus far. Rather we nowadays see a wide variety of handy applications covering selected disciplines. At the same time, we face a big data reality where clinical details have expanded due to technological advances such as genotyping and sophisticated imaging. These developments herald the (re)introduction of highly specialized centers for computing and data interpretation, thus paving the way to translate vast amounts of information into knowledge. The scope is enlarged to astronomical proportions, but the promise is still the same as in the 1990s.

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Feature Selection for High-Dimensional Data

Cited by 184 publications

References 69 publications

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Variable Selection for Road Segmentation in Aerial Images

Medical Expert Systems

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