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Explainable Ensemble Machine Learning Method for Credit Risk Classification
2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Year: 2024 | Conference Paper |
Credit risk classification (CRC) is a crucial task for banks to determine the financial position of the client for credit. Several machine learning models were proposed to deal with credit risk classification. However, conventional methods operate as black-box models that provide only the classification of clients without providing further explanations. To address this issue, we propose an explain...Show More
A Novel Tweets Clustering Method using Word Embeddings
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)
Year: 2018 | Conference Paper |
Cited by: Papers (8)
Twitter users share a variety of information discussing multiple topics. Clustering-based methods have become an effective solution to group together tweets related to the same topics. However, due to the lack of context, short texts are challenging to model. Most of the existing clustering methods use the Vector Space Model (VSM) to transform tweets into a structured form. However, this represent...Show More
Learning Probabilistic Relational Models with (Partially Structured) Graph Databases
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)
Year: 2017 | Conference Paper |
Probabilistic Relational Models (PRMs) such as Directed Acyclic Probabilistic Entity Relationship (DAPER) models are probabilistic models dealing with knowledge representation and relational data. Existing literature dealing with PRM and DAPER relies on well structured relational databases. In contrast, a large portion of real-world data is stored in Nosql databases specially graph databases that ...Show More
KP-S: A Spark-Based Design of the K-Prototypes Clustering for Big Data
Mohamed Aymen Ben HajKacem;Chiheb Eddine Ben N'Cir;Nadia Essoussi
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Big data is often characterized by a huge volume and a mixed types of attributes namely, numeric and categorical. K-prototypes is one of the most well-known clustering methods to deal with mixed data. Several parallel alternatives based on MapReduce have been proposed to enable this method to handle large scale of mixed data. However, these solutions are not suitable when dealing with Big data, du...Show More
Parallel clustering method for non-disjoint partitioning of large-scale data based on spark framework
2016 IEEE International Conference on Big Data (Big Data)
Year: 2016 | Conference Paper |
Cited by: Papers (4)
Clustering large scale data has become an important challenge which motivates several recent works. While the emphasis has been on the organization of massive data into disjoint groups, this work considers the identification of non-disjoint groups rather than the disjoint ones. In this setting, it is possible for data object to belong simultaneously to several groups since many real-world applicat...Show More
Overlap regulation for additive overlapping clustering methods
2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
Overlapping Clustering is an important technique in machine learning which aims to organize data into a set of non-disjoint groups rather than the disjoint one which is the case of conventional clustering methods. Several machine learning applications require that data object be assigned to one or several groups resulting in non-disjoint partitioning of data such as document clustering where each ...Show More
MapReduce-based k-prototypes clustering method for big data
Mohamed Aymen Ben Haj Kacem;Chiheb-Eddine Ben N'cir;Nadia Essoussi
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2015 | Conference Paper |
Cited by: Papers (14)
Big data clustering is one of the recently challenging tasks that is used in many application domains. Traditional clustering methods are not able to deal with large-scale of data. Furthermore, Big data are often characterized by the mixed type of data, including numerical and categorical attributes. Thus, we propose in this paper the parallelization of k-prototypes clustering method (MR-KP) using...Show More
Data mining techniques to predict protein secondary structures
2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)
Year: 2013 | Conference Paper |
Cited by: Papers (2)
Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequ...Show More
Identification of non-disjoint clusters with small and parameterizable overlaps
2013 International Conference on Computer Applications Technology (ICCAT)
Year: 2013 | Conference Paper |
Cited by: Papers (4)
Identification of non-disjoint groups in unlabeled data sets is an important issue in clustering. Many real life applications require to find overlapping clusters in order to fit the data set structures such as clustering of films where each film can have different genres. This paper presents an overlapping k-means method refereed as Restricted-OKM (Restricted Overlapping k-means) that generalizes...Show More
DM-pred Method: A New Method to Predict Secondary Structures Based on Data Mining Techniques
2011 22nd International Workshop on Database and Expert Systems Applications
Year: 2011 | Conference Paper |
Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequ...Show More
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