An intelligent quality-based approach to fusing multi-source possibilistic information

Abstract

Recently, Yager and Petry were proposing a quality-based methodology to combine data provided by multiple probabilistic sources to improve the quality of information for decision-makers. This paper offers a sort of companion paper that adapts this methodology to possibilistic sources. Possibility theory is particularly well suited to cope with incomplete information from poor-data sources. The methodology and algorithms used for the probabilistic approach are adapted for the possibilistic case. Both approaches are then compared by the means of a numerical example and four experimental benchmark datasets: one, the IRIS data set, being data-poorer than the three other ones (Diabetes dataset, Glass dataset and Liver-disorder dataset). A vector representation is introduced for a possibility distribution as in the probabilistic case and, the Gini's formulation of entropy is being used. However, the Gini's entropy has to be used differently than with the probabilistic case. This has an impact on the selection of subsets. A fusion scheme is designed to select the best-quality subsets according to two information quality factors: quantity of information and source credibility. Results obtained from comparison of both approaches on the four experimental benchmarks confirm the superiority of the possibilistic approach in the presence of information scarcity or incompleteness.

Multisource basic probability assignment fusion based on information quality

“…To understand the recognition rate of this method on the iris recognition data set, the coefficient in the method of Kang et al is set to α = 5. After testing 150 samples of statistics, the results in the Table 12 can be obtained and compared with the other methods, such as the method of Kang et al, SVM classifier and Probabilistic method in Reference [2]. The result of the proposed method is that the recognition rate is 96.67%, which is higher than other methods.…”

“…For this reason, Yager and Petry 1 proposed the quality of information under the framework of probability theory and using information quality and credibility function as the standard to determine the most valuable information set used in task execution. Subsequently, Bouhamed et al 2 introduced information quality under the framework of possibility theory 3,4 and proposed an eight-step method to identify the most valuable information set.…”

Information Quality Assessment for Data Fusion Systems

“…Additionally, such model sets out some recommendations such as to offer a justification when an IQ criterion is excluded and to describe their categorizations. Intelligent quality-based approach [130] fusing multi-source possibilistic information 0 0 3 2020 Dynamic weight allocation method [12] multisource information fusion 0 0 0 2021…”

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS

“…Thus, recent works propose image filtering to reject the input images whose quality does not meet a minimum quality requirement. Fingerprint image quality remains a significant challenge, even though several measurement processes have been proposed in the literature [ 10 , 11 , 12 , 13 , 14 ].…”