Subspace Clustering of High-Dimensional Data: An Evolutionary Approach

Singh, Vijendra; Sahoo, Laxman

doi:10.1155/2013/863146

Cited by 9 publications

(6 citation statements)

References 39 publications

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“…Thus, it ensured that np number of the memory pool is created by running the AIRS algorithm on each process. As a result, the memory pools obtained are merged [18].…”

Section: Methods a Artificial Immune Recognition System (Airs)mentioning

confidence: 99%

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Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Şahin

Di̇ri̇

2019

IEEE Access

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Stability and robustness of feature selection techniques have great importance in the high dimensional and small sample data. The neglected subject in the feature selection is solving the instability problem. Therefore, an ensemble gene selection framework is used in order to provide stable and accurate results of feature selection algorithms. Sequence modeling from high-dimensional data is an important research area for the discovery of biomarkers. Identifying biomarkers requires robust gene selection methods, which makes it possible to find important tumor-related genes with high accuracy. The main issue of this paper is creating a model in order to learn long sequences with the artificial immune recognition system (AIRS) for robust feature selection. Long short-term memory (LSTM) recurrent neural networks are trained with the AIRS in order to obtain the long-lived unit cells for use in the feature selection process. LSTM was used to be better understanding the mechanisms involving the ''remember'' feature of the immunological behavior of the immune response. We tried to apply a theory suggested by immunologists in order to develop stable associative memory, which capable of solving robustness and optimization tasks. We examined the initial gene selection step based on the different types of group formation algorithm for analysis of the most informative selected features. Microarray datasets are showing remarkable increases in their robustness and classification accuracy. The suggested framework is evaluated on six commonly used microarray datasets.

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“…Thus, it ensured that np number of the memory pool is created by running the AIRS algorithm on each process. As a result, the memory pools obtained are merged [18].…”

Section: Methods a Artificial Immune Recognition System (Airs)mentioning

confidence: 99%

“…The conditional entropy computes the dependencies of features with the (16). If X is a random variable and P(x) is the probability of x, the information gain is computed with the (17), SU value computed with using the equation of (18).…”

Section: Experiments Setupmentioning

confidence: 99%

Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Şahin

Di̇ri̇

2019

IEEE Access

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“…However, the designed algorithm needs large amount of memory space. A robust multi objective subspace clustering (MOSCL) algorithm was presented in [21] for high-dimensional clustering with higher accuracy of subspace clustering. But, the space complexity remained unaddressed using MOSCL algorithm.Graph-based clustering was developed in [22] to cluster the web search results with high clustering quality.…”

Section: Related Workmentioning

confidence: 99%

Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

Pushpalatha¹,

Sundaram²

2018

IJECE

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<p>Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-art-works.</p>

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“…In the experiments, the relative efficiency of the proposed evolutionary-incremental framework for feature selection was evaluated. The proposed framework can also be used for other optimization problems such as clustering which has variable length solutions [16,17].…”

Section: %mentioning

confidence: 99%

A New Evolutionary-Incremental Framework for Feature Selection

Sigari

Pourshahabi

Pourreza

2014

Advances in Artificial Intelligence

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Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

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Subspace Clustering of High-Dimensional Data: An Evolutionary Approach

Cited by 9 publications

References 39 publications

Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Robust Feature Selection With LSTM Recurrent Neural Networks for Artificial Immune Recognition System

Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

A New Evolutionary-Incremental Framework for Feature Selection

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