The return of jedAI

Papadakis, George; Tsekouras, Leonidas; Thanos, Emmanouil; Giannakopoulos, George; Palpanas, Themis; Koubarakis, Manolis

doi:10.14778/3229863.3236232

Cited by 50 publications

(2 citation statements)

References 16 publications

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“…For instance, Dong and her collaborative researchers proposed a probabilistic schema integration approach in [32], as well as data fusion approaches that consider data sources correlation in sort of data duplication [23][24][25]37]. Moreover, research efforts related to probabilistic entity linkage and resolution can be found at [9,10,[38][39][40][41][42][43][44][45][46].…”

Section: Data Integrationmentioning

confidence: 99%

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Jaradat

Safieddine

Deraman

et al. 2022

BDCC

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Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence.

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Section: Data Integrationmentioning

confidence: 99%

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Jaradat

Safieddine

Deraman

et al. 2022

BDCC

View full text Add to dashboard Cite

show abstract

“…Without a doubt, the last two decades have been characterized by massive production of data with regards to the fields of Computer Science (CS) and Artificial Intelligence (AI). Several real-life applications contribute to this phenomenon, operating as rich sources of data over all possible kinds: structured, semi-structured or unstructured [1,2]. We distinguish the next fields: social media platforms, economic transactions, medical recordings and Internet of Things (IoT) where Industry 4.0, constitutes a highly affected application coming from the latter field [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

2020

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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.

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Address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach

Guermazi

Sellami

Boucelma

2020

ECML PKDD 2020 Workshops

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The return of jedAI

Cited by 50 publications

References 16 publications

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

Address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach

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