Multicriteria fuzzy classification procedure PROCFTN: methodology and medical application

Belacel, Nabil; Boulassel, Mohamed Rachid

doi:10.1016/s0165-0114(03)00022-8

Cited by 42 publications

(24 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…NN techniques have been frequently used for prediction (Radhika & Shashi, 2009;Smith et al, 2009), pattern recognition (Isa & Mamat, 2011), signal processing (Li & Adali, 2008;Nielsen et al, 2009) and classification (Belacel & Boulassel, 2004;Yuan-Pin et al, 2007). NN present a number of advantages over conventional empirical methods.…”

Section: Problem Statementsmentioning

confidence: 99%

Jordan Pi-Sigma Neural Network for Temperature Prediction

Husaini

Ghazali

Nawi

et al. 2011

Communications in Computer and Information Science

View full text Add to dashboard Cite

In recent years, various temperature forecasting models have been proposed, which broadly can be classified into physically-based approaches and statistically-based approaches. Hitherto, those approaches involve sophisticated mathematical models to justify the use of empirical rules which make them less desirable for some applications. Therefore, in this respect, Neural Networks (NN) have been successfully applied and with no doubt, they provide the ability and potentials to predict the temperature events. However, the ordinary NN adopts computationally intensive training algorithms and can easily get trapped into local minima.To overcome such drawbacks in ordinary NN, this research focuses on using a

show abstract

Section: Problem Statementsmentioning

confidence: 99%

Jordan Pi-Sigma Neural Network for Temperature Prediction

Husaini

Ghazali

Nawi

et al. 2011

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…The fuzzy indifference relation is based on the concordance and non-discordance principle which represents the relationship (membership degree) between the object to be assigned and the prototype [7,10]; it is formulated as: where w jh is the weight that measures the importance of a relevant attribute g j of a specific class C h :…”

Section: Computing the Fuzzy Indifference Relation: I(a B Hmentioning

confidence: 99%

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

show abstract

“…On these cases a different approach like a fuzzy logic system is needed. Fuzzy systems have been applied for a number of different problems of classification such as medical applications [8], soil classification [9], fish grading [10], etc. For the crater classification process, a number of problems are inherited from the ambiguity and vagueness of the definitions, and because of this, the use of a fuzzy system was considered.…”

Section: Crater Classification Subsystemmentioning

confidence: 99%

Crater Marking and Classification Using Computer Vision

Flores-Mendez

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In the last three years NASA and some other Space Agencies have draw some interest to date Mars surface, mainly because the relationship between its geological age and the probable presence of water beneath it. One way to do this is by classifying craters on the surface attending to their degree of erosion. The naïve way to solve this problem would let a group of experts analyze the images of the surface and let them mark and classify the craters. Unfortunately, this solution is unfeasible because the number of images is huge in comparison with the human resources any group can afford. Different solutions have been tried [1], [2] over this period of time. This paper offers an autonomous Computer Vision System to detect the craters, and classify them.

show abstract

Multicriteria fuzzy classification procedure PROCFTN: methodology and medical application

Cited by 42 publications

References 25 publications

Jordan Pi-Sigma Neural Network for Temperature Prediction

Jordan Pi-Sigma Neural Network for Temperature Prediction

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Crater Marking and Classification Using Computer Vision

Contact Info

Product

Resources

About