Manipulating Data and Dimension Reduction Methods: Feature Selection

Liu, Huan; Zhao, Zheng

doi:10.1007/978-0-387-30440-3_317

Cited by 44 publications

(43 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second style is called the wrapper method, which directly connects the selection of the feature subset to the performance of some specific classifier, such as an SVM, decision tree, or random forest. The filtering-based approach is more universal and is independent of classifiers, whereas the wrapper-based approach tends to perform better in practice [67,69]. Both styles rely on efficient optimization algorithms.…”

Section: Curse Of Dimensionalitymentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Q¹,

Zhao²,

Huang³

et al. 2017

Preprint

View full text Add to dashboard Cite

The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

show abstract

Section: Curse Of Dimensionalitymentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Q¹,

Zhao²,

Huang³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…This approach which is called Wrapper approach has been used with most of the classification algorithms such as decision tree (DT), support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbor (KNN), artificial neural networks (ANNs), and linear discriminant analysis (LDA), have been applied to wrappers for feature selection [13], [14], [15].…”

Section: Related Work 21 Gp and Feature Extractionmentioning

confidence: 99%

Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection

Badran¹,

Rohim²

2017

IJCA

View full text Add to dashboard Cite

In this paper, we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without utilizing multi-objective genetic programming in the feature extraction phase. This work argues that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion "KDD-99 cup" dataset. Then, the experiments were repeated by adding the feature extraction phase. The results of the two approaches are compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.

show abstract

“…Filter algorithms are often computationally less expensive and more general than wrapper algorithms. However, filters ignore the performance of the selected features on a classification algorithm, whereas wrappers evaluate the feature subsets based on the classification performance, which usually results in better performance achieved by wrappers than filters for a particular classification algorithm [1], [7], [8]. Note that some researchers categorize feature selection methods into three groups: 1) wrapper; 2) embedded; and 3) filter approaches [7], [8].…”

Section: IImentioning

confidence: 99%

“…There are very few feature selection methods that use an exhaustive search [1], [7], [8]. This is because even when the number of features is relatively small (e.g., 50), in many situations, such methods are computationally too expensive to perform.…”

Section: Existing Work On Feature Selection 1) Search Techniquesmentioning

confidence: 99%

“…This paper focuses mainly on feature selection for classification since there is much more work on feature selection for classification than for other tasks [1]. Recent reviews on feature selection can be seen in [7], [8], [12], [13] which focus mainly on non-EC-based methods. De la Iglesia [14] presented a summary of works using EC for feature selection in classification, which is suitable for a non-EC audience since it focuses on basic EC concepts and genetic algorithms (GAs) for feature selection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation