UFSSF - An Efficient Unsupervised Feature Selection for Streaming Features

Almusallam, Naif; Tari, Zahir; Chan, Jeffrey; Alharthi, Adil Fahad

doi:10.1007/978-3-319-93037-4_39

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…In the unsupervised feature selection algorithm of the past two years, Almusallam et al proposed an efficient unsupervised feature selection for streaming features [8]. The algorithm uses the K-means algorithm to aggregate features that are not known into a feature stream.…”

Section: -Related Workmentioning

confidence: 99%

“…The unsupervised feature selection algorithm mainly mines more representative features in the data. In the absence of the class label Y, using the data matrix X as a response matrix, the internal structure of the original features of the data can be better preserved [18,19]. In order to fully exploit the nonlinear relationship of data features.…”

Section: -2-lsk Fs Algorithmmentioning

confidence: 99%

“…After fixing A, α and B, the objective function (7) (15) We first calculate the Euclidean distance between every two data features to construct the neighbours of all the features. If the j-th feature does not belong to the nearest neighbour of the i-th feature, then the value of , ij s is zero; otherwise, the value of , ij s is solved by equation (18). At the same time, optimizing S is equivalent to optimizing each ( 1,..., )…”

Section: X X Pab B a P X B A P Pab B A Xlx Abmentioning

confidence: 99%

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Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

Wen

Gan

et al. 2019

Emerg Sci J

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In this paper, we propose a new unsupervised feature selection algorithm by considering the nonlinear and similarity relationships within the data. To achieve this, we apply the kernel method and local structure learning to consider the nonlinear relationship between features and the local similarity between features. Specifically, we use a kernel function to map each feature of the data into the kernel space. In the high-dimensional kernel space, different features correspond to different weights, and zero weights are unimportant features (e.g. redundant features). Furthermore, we consider the similarity between features through local structure learning, and propose an effective optimization method to solve it. The experimental results show that the proposed algorithm achieves better performance than the comparison algorithm.

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Section: -Related Workmentioning

confidence: 99%

Section: -2-lsk Fs Algorithmmentioning

confidence: 99%

Section: X X Pab B a P X B A P Pab B A Xlx Abmentioning

confidence: 99%

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Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

Wen

Gan

et al. 2019

Emerg Sci J

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“…The proposed approach is an extension of our previously published work [10]. This approach extends the k-mean clustering to cumulatively determine whether a newly-arrived feature can be selected as a representative streaming feature.…”

Section: Introductionmentioning

confidence: 99%

Towards an Unsupervised Feature Selection Method for Effective Dynamic Features

et al. 2021

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Dynamic features applications present new obstacles for the selection of streaming features. The dynamic features applications have various characteristics: a) features are processed sequentially while the number of instances is fixed; and b) the feature space does not exist in advance. For example, in a text classification task for spam detection, new features (e.g. words) are dynamically generated and therefore need to be mined to filter out the spams rather than waiting for all features to be collected in order to do so. Traditional feature selection methods, which are not designed for streaming features applications, cannot be used in such an environment, as they require the full feature space in advance in order to statistically determine the representative features. Existing methods that address feature selection in dynamic features applications require the class labels in order to select the representative features. However, most of the reallife data is unlabeled and it is costly to apply manual labeling. In this paper, an efficient unsupervised features selection method is proposed for streaming features applications where the number of features increases while the number of instances remains fixed. In particular, unsupervised Feature Selection for Dynamic Features (UFSSF) is developed to determine the representative streaming features without requiring prior knowledge about data class labels or representative features. The UFSSF extends the k-mean clustering to cumulatively determine whether the newly-arrived feature can be selected as a representative streaming feature, or discarded. Experimental results show significant accuracy results and efficient execution time compared to those of other benchmark methods.INDEX TERMS feature selection, streaming features, unsupervised learning.

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“…For example, Lin et al [29] proposed a fuzzy mutual information criterion for streaming feature selection and multi-label learning. Also, Almusallam et al [30] introduced an efficient algorithm for unsupervised feature selection for streaming features. However, both methods fail to consider the relationships and interactions of features in the feature selection process [31,32].…”

Section: Introductionmentioning

confidence: 99%

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

Cheng

2019

Entropy

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In recent years, there has been a growing interest in the problem of multi-label streaming feature selection with no prior knowledge of the feature space. However, the algorithms proposed to handle this problem seldom consider the group structure of streaming features. Another shortcoming arises from the fact that few studies have addressed atomic feature models, and particularly, few have measured the attraction and repulsion between features. To remedy these shortcomings, we develop the streaming feature selection algorithm with dynamic sliding windows and feature repulsion loss (SF-DSW-FRL). This algorithm is essentially carried out in three consecutive steps. Firstly, within dynamic sliding windows, candidate streaming features that are strongly related to the labels in different feature groups are selected and stored in a fixed sliding window. Then, the interaction between features is measured by a loss function inspired by the mutual repulsion and attraction between atoms in physics. Specifically, one feature attraction term and two feature repulsion terms are constructed and combined to create the feature repulsion loss function. Finally, for the fixed sliding window, the best feature subset is selected according to this loss function. The effectiveness of the proposed algorithm is demonstrated through experiments on several multi-label datasets, statistical hypothesis testing, and stability analysis.

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UFSSF - An Efficient Unsupervised Feature Selection for Streaming Features

Cited by 6 publications

References 8 publications

Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

Sparse Nonlinear Feature Selection Algorithm via Local Structure Learning

Towards an Unsupervised Feature Selection Method for Effective Dynamic Features

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

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