Differential Evolution for learning the classification method PROAFTN

Al-Obeidat, Feras; Belacel, Nabil; Carretero, Juan A.; Mahanti, Prabhat Kumar

doi:10.1016/j.knosys.2010.02.003

Cited by 29 publications

(15 citation statements)

References 27 publications

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“…The first study compares the performance of PSOPRO against the previous work applied on PROAFTN method [1,2,3]. Secondly, further experimental work was conducted with six machine learning techniques.…”

Section: Comparative Studymentioning

confidence: 99%

“…Table 7 documents the performance of PSOPRO against the previous work done on PROAFTN. In this regards, DEPRO is the abbreviation of using Differential Evolution for learning PROAFTN which has been explained in [3]. The term PSOPRO-RVNS represents the utilization of PSO and Reduced Variable Neighborhood Search (RVNS) for learning PROAFTN, more details are explained in [1,2].…”

Section: Comparative Studymentioning

confidence: 99%

“…MCDA methods have been widely used in many research fields; they are categorized into three fields: (1) Value measurement models (AHP [26] is a well known method belonging to this category); (2) Goal, aspiration and reference level models (Goal programming [12] is an example of this group); and finally (3) Outranking models (ELECTRE [20], PROMETHEE [49] and PROAFTN [9] are the methods that belong to this group).…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the performance of PSOPRO is compared with the previous work applied on PROAFTN [1,2,3]. Finally, the efficiency of PSOPRO is evaluated against six machine learning classifiers, chosen from different machine learning perspectives including: Logical/Symbolic techniques such as Decision Tree (C4.5 [38]), Statistical learning algorithms (e.g., Naive Bayes (NB [18])), Support Vector Machine (SVM [13]), Perceptron-based techniques (e.g., Neural Networks (NN) [15]), Instance-based learning (e.g., k-nearest neighbor (k-nn) [46]), and the rule-based classifiers such as PART [21,51].…”

Section: Introductionmentioning

confidence: 99%

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An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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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.

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Section: Comparative Studymentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

Self Cite

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“…Then they showed in a second study [10] that their method based on genetic algorithm gives better performance than the AHP method. Al-Obeidat et al [1] proposed an hybrid model which combine Differential Evolution method with the PROAFTN method, by using the evolutionary algorithm to inductively obtain PROAFTN's parameters from data to achieve a high classification accuracy. Liu et al [21] combined the methods of Electre III and the simulating annealing to deal with the compensatory effect of the items against criteria and opted for grouping criteria.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Cherif

Ladhari

2016

Communications in Computer and Information Science

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Abstract. ABC analysis is a well-established categorization technique based on the Pareto Principle which dispatches all the items into three predefined and ordered classes A, B and C, in order to derive the maximum benefit for the company. In this paper, we present a new approach for the ABC Multi-Criteria Inventory Classification problem based on the Artificial Bee Colony (ABC) algorithm with the Multi-Criteria Decision Making method namely VIKOR. The ABC algorithm tries to learn and optimize the criteria weights, which are then used as an input parameters by the method VIKOR. The MCDM method generates a ranking items and therefore an ABC classification. Each established classification is evaluated by an estimation function, which also represents the objective function. The results of our proposed approach were obtained from a widely used data set in the literature, and outperforms the existing classification models from the literature, by obtaining better inventory cost.

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Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

Al-Obeidat

Belacel

Spencer

2019

Lecture Notes in Computer Science

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The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems.

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Differential Evolution for learning the classification method PROAFTN

Cited by 29 publications

References 27 publications

An evolutionary framework using particle swarm optimization for classification method PROAFTN

An evolutionary framework using particle swarm optimization for classification method PROAFTN

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

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