The image reduction process based on Generalized Mixture functions

Farias, Antonio Diego Silva; Costa, Valdigleis S.; Santiago, Regivan H. N.; Bedregal, Benjamín R. C.

doi:10.1109/nafips.2016.7851591

Cited by 6 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is a GM function. [30] and [32]. In this paper, we use the H Med , H Arith and H Max functions as a combination method to combine the output of ensembles.…”

Section: Proposition 34 H Is a Preaggregation Functionmentioning

confidence: 99%

“…In this paper, we use the H Med , H Arith and H Max functions as a combination method to combine the output of ensembles. For a more comprehensive study about the generalized mixture functions, readers are strongly suggested to refer to [29,30,31,32,33,55,56].…”

Section: Proposition 34 H Is a Preaggregation Functionmentioning

confidence: 99%

“…The main advantage of GM functions is that the weights of its inputs can be dynamically defined for each input. In other words, while the weights of the other weighted functions are assigned statically, without taking into account the testing patterns, the generalized mixture functions can assign weights as a function of the testing patterns [32]. In practical applications, the GM functions have presented good results in the tasks of noise treatment and image reduction [29,30,32].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

Self Cite

View full text Add to dashboard Cite

Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixture (GM) functions as a new approach for providing an efficient combination procedure for these systems through the use of dynamic weights in the combination process. Therefore, we present three GM functions to be applied as a combination method. The main advantage of these functions is that they can define dynamic weights at the member outputs, making the combination process more efficient. In order to evaluate the feasibility of the proposed approach, an empirical analysis is conducted, applying classifier ensembles to 25 different classification data sets. In this analysis, we compare the use of the proposed approaches to ensembles using traditional combination methods as well as the state-of-the-art ensemble methods. Our findings indicated gains in terms of performance when comparing the proposed approaches to the traditional ones as well as comparable results with the state-of-the-art methods. This paper is divided into eight sections and it is organized as follows. In Section 2, we describe some recent studies in classifier ensembles. The fundamental notions of the generalized mixture functions are introduced in Section 3, while the basic concepts of classifier ensembles are presented in Section 4. The proposed approach is presented in Section 5. In Section 6, the experimental methodology is presented, while Section 7 presents an analysis of the obtained results of this work. Finally, Section 7 concludes this paper. Recent Studies in Weighted Combination Methods for in Classifier EnsemblesAs already mentioned, different ways of calculating the weights of each class for each individual classifier can be used in a classifier ensemble [10,39,45,46,50,51,57,70]. Although weighted combination methods appear to provide some flexibility, obtaining the optimal weights is not an easy task. Therefore, some optimization techniques have been applied to define the best set of weights, such as in [4,45,51,57]. In [57], for instance, a genetic algorithm (GA) was used to define an optimized set of weights that are used along with the output of the individual classifiers to define the final output of the classifier ensembles. However, all the aforementioned studies apply procedures to define static weights. In other words, these methods define a set of weights that are used throughout the testing phase. This static way to define weights can eventually become inefficient for a classifier ensemble, since the accuracy of an individual classifier can change in the testing search space and this change is not capture by static weights.In a dynamic weighting process, the outputs of all individual classifiers are aggregated and the most competent ones receive the highest weight values. The competence of the classifier o...

show abstract

“…is a GM function. [30] and [32]. In this paper, we use the H Med , H Arith and H Max functions as a combination method to combine the output of ensembles.…”

Section: Proposition 34 H Is a Preaggregation Functionmentioning

confidence: 99%

Section: Proposition 34 H Is a Preaggregation Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining multiple algorithms in classifier ensembles using generalized mixture functions

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Proof Using Remark 3, substitution l = 1 c − 1 and condition (21), we get immediately interval (22). For c = 0, M g is the arithmetic mean.…”

Section: Example 32mentioning

confidence: 90%

“…The authors in [19] studied also directional and ordered directional monotonicity of the generalized mixture functions, and determined some criteria for obtaining generalized mixture functions and so-called bounded generalized mixture functions. Applications of the mentioned generalized mixture functions in machine learning and classication can be found, for example, in [17] and [21].…”

Section: Introductionmentioning

confidence: 99%

Mixture functions and their monotonicity

Špirková

Beliakov

Bustince

et al. 2019

Information Sciences

View full text Add to dashboard Cite

We consider mixture functions, which are a type of weighted averages for which the corresponding weights are calculated by means of appropriate continuous functions of their inputs. In general, these mixture function need not be monotone increasing. For this reason we study sucient conditions to ensure standard, weak and directional monotonicity for specic types of weighting functions. We also analyze directional monotonicity when dierentiability is assumed.

show abstract

Distance measures on intuitionistic hesitant fuzzy set and its application in decision-making

Chen

Suo

2021

Comp. Appl. Math.

View full text Add to dashboard Cite

The image reduction process based on Generalized Mixture functions

Cited by 6 publications

References 29 publications

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Combining multiple algorithms in classifier ensembles using generalized mixture functions

Mixture functions and their monotonicity

Distance measures on intuitionistic hesitant fuzzy set and its application in decision-making

Contact Info

Product

Resources

About