Feature selection via Boolean independent component analysis

Apolloni, Bruno; Bassis, Simone; Brega, Andrea

doi:10.1016/j.ins.2009.07.002

Cited by 12 publications

(6 citation statements)

References 68 publications

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“…In the last decade, metaheuristic methods such as tabu search [55,62], simulated annealing [10,33], genetic algorithms [17,42,47,54,57,61] have also been applied to solve the feature subset selection problem. In addition, there are feature selection methods based on rough set theory [4,7,19,58,63] and on Boolean independent component analysis [1].…”

Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

Ünler¹,

Murat

Chinnam

2011

Information Sciences

281

115

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Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

Ünler¹,

Murat

Chinnam

2011

Information Sciences

281

115

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“…The family is named BICA [16] and the algorithms for extracting these components are based on peculiar neural networks; Fig. 4.…”

Section: The Pram Architecturementioning

confidence: 99%

Toward a cooperative brain: Continuing the work with John Taylor

Apolloni

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

Self Cite

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I propose a three-step discussion following a research path shared in part with John Taylor where the leitmotif is to understand the cooperation between thinking agents: the pRAM architecture, the butler paradigm, and the networked intelligence. All three steps comprise keystones of European projects which one of us has coordinated. The principled philosophy is to "start simple and insert progressive complexity". The results I discuss only go as far as the "start simple" point. The final goal is to find a bias that underpins the entire research effort. In this paper I will move within the connectionist paradigm at various scales, the largest being one that encompasses an Internet of Things instantiation.

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“…On the other hand, filter method uses some quality measure which is independent of the performance of the learning algorithm [8,9]. Apolloni et al [12] present BICA (Boolean independent components analysis) for extracting bits from the original features. It looks for Boolean independent variables along with minimal joint entropy and consistent assignments and produces a vector of Boolean variables whose assignments reflect the relevant features of the original data set, under the consistency control that the same assignment is not generated by data set having different classification labels.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Selection with Efficient Initialization of Clusters Centers for High Dimensional Data Clustering

Rajput

Singh

Bhattacharya

2011

2011 International Conference on Communication Systems and Network Technologies

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Most of the traditional data clustering algorithms suffer from two main problems (i) the curse of dimensionality and (ii) random initialization of clusters centers which leads to local optimum clustering. In this paper, we propose a technique for selecting most relevant dimensions of data set and efficient initialization of clusters centers. Our proposed technique uses the median absolute deviation (MAD) for selection of relevant dimensions of data set and then it uses most frequent value (MODE) of selected dimensions to determine the initial clusters centers. Finally these initial clusters centers are used in the k-means algorithm for optimum clustering. Empirical results show that the algorithm produces comparatively efficient results. The quality measures also validate good quality of the obtained results.

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Feature selection via Boolean independent component analysis

Cited by 12 publications

References 68 publications

mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

Toward a cooperative brain: Continuing the work with John Taylor

Feature Selection with Efficient Initialization of Clusters Centers for High Dimensional Data Clustering

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