Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data

Sánchez, Luciano; SUAREZ, M; Villar, José R.; Couso, Inés

doi:10.1109/fuzzy.2007.4295665

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The relevance of the inputs is studied using FMI in Ref. [11] and thus discriminated the highly informative variables from apparently less informative ones, thus using FMI as a possibility-based measure. It provides more stable and accurate estimations regarding the relation between features [14].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Mutual Information-Based Intraslice Grouped Ray Casting

Mehaboobathunnisa¹,

Thasneem²,

Sathik³

2017

Journal of Intelligent Systems

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The traditional ray casting algorithm has the capability to render three-dimensional volume data in the viewable two-dimensional form by sampling the color data along the rays. The speed of the technique relies on the computation incurred by the huge volume of rays. The objective of the paper is to reduce the computations made over the rays by eventually reducing the number of samples being processed throughout the volume data. The proposed algorithm incorporates the grouping strategy based on fuzzy mutual information (FMI) over a group of voxels in the conventional ray casting to achieve the reduction. For the data group, with FMI in a desirable range, a single primary ray is cast into the group as a whole. As data are grouped before casting rays, the proposed algorithm reduces the interpolation calculation and thereby runs with lesser complexity, preserving the image quality.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Mutual Information-Based Intraslice Grouped Ray Casting

Mehaboobathunnisa¹,

Thasneem²,

Sathik³

2017

Journal of Intelligent Systems

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“…The ideas and principles previously shown have been used in several applications with low quality data, with both realistic and real world data sets. [14,15,19].…”

Section: Issues In Low Quality Data Managementmentioning

confidence: 99%

Analysing the Low Quality of the Data in Lighting Control Systems

Villar

Cal

Sedano

et al. 2010

Lecture Notes in Computer Science

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Abstract. Energy efficiency represents one of the main challenges in the engineering field, i.e., by means of decreasing the energy consumption due to a better design minimising the energy losses. This is particularly true in real world processes in the industry or in business, where the elements involved generate data full of noise and biases. In other fields as lighting control systems, the emergence of new technologies, as the Ambient Intelligence can be, degrades the quality data introducing linguistic values. The presence of low quality data in Lighting Control Systems is introduced through an experimentation step, in order to realise the improvement in energy efficiency that its of managing could afford. In this contribution we propose, as a future work, the use of the novel genetic fuzzy system approach to obtain classifiers and models able to deal with the above mentioned problems.

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“…In previous works [29] we have defined the mutual information between a random variable X and a random set C as the set of all the values of mutual information between the variable X and each one of the selections of C:…”

Section: Mutual Information Between a Random Variable And A Fuzzy Ranmentioning

confidence: 99%

Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data

Sánchez

SUAREZ

Villar

et al. 2008

International Journal of Approximate Reasoning

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Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems.

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Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data

Cited by 9 publications

References 17 publications

Fuzzy Mutual Information-Based Intraslice Grouped Ray Casting

Fuzzy Mutual Information-Based Intraslice Grouped Ray Casting

Analysing the Low Quality of the Data in Lighting Control Systems

Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data

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