Hassina Seridi

Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines

This paper presents a new approach that avoids the over-fitting and complexity problems suffered in the construction of decision trees. Decision trees are an efficient means of building classification models, especially in industrial engineering. In their construction phase, the two main problems are choosing suitable attributes and database components. In the present work, a combination of attribute selection and data sampling is used to overcome these problems. To validate the proposed approach, several experiments are performed on 10 benchmark datasets, and the results are compared with those from classical approaches. Finally, we present an efficient application of the proposed approach in the construction of non-complex decision rules for fault diagnosis problems in rotating machines.

Genetic Optimization of Decision Tree Choice for Fault Diagnosis in an Industrial Ventilator

Fault diagnosis and condition monitoring of industrial machines have known significant progress in recent years, particularly with the introduction of pattern recognition and data-mining techniques for their development. The decision trees are among the most suitable techniques for the diagnosis and have several algorithms for their construction. Each building algorithm has its advantages and drawbacks which make the optimal choice of adapted method to the desired application difficult. In this paper we propose the diagnosis accomplishment of an industrial ventilator based on the combination vibration analysis-decision trees. For the choice of the adapted decision tree building algorithm a method based on genetic algorithms was used. Its results were commented and discussed

Solearn: A Social Learning Network

We have assisted the development of a significant number of e-learning systems, which have achieved great success in distance teaching and education, but most of these systems present some limitations and some disadvantages. Most of them are closed where learning resources are fixed and the adaptability, the flexibility, and social relations are ignored and in most cases are not taken into account at all, actors of such systems tend to have a minimal collaborative navigation, awareness features and social relations analysis and they often find themselves isolated without sensing what the rest of learning community is doing. Significantly, new technologies had recently emerged: the social concepts and the social awareness features leading significant change to collaboration and learning. These emerging technologies are increasingly being adopted to improve remote education and providing better enhancement for learning. These improvements are offered to students who, regardless of their computer systems, can collaborate to improve their cognitive and social skills. In this article, we present the concepts of a new learning paradigm: CSSL (Computer Supported Social Learning) and we have implemented a first prototype called SoLearn that groups some of those concepts. SoLearn (A Social Learning Network) aims to provide its users with a new learning experience based on social networks and enhanced with social awareness concepts.

Improving Memory-Based User Collaborative Filtering with Evolutionary Multi-Objective Optimization

Abstract The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on improving as much as possible the accuracy by recommending familiar items (i.e., popular items over the group). Yet, this may reduce the number of items that could be recommended and consequently weakens the chances of recommending novel items. To address this problem, it is desirable to consider recommendation coverage when selecting the appropriate group. This could help in simultaneously making both accurate and diverse recommendations. In this paper, we propose to focus mainly on the growing of the large search space of users’ profiles and to use an evolutionary multi-objective optimization-based recommendation system to pull up a group of profiles that maximizes both similarity with the active user and diversity between its members. In such manner, the recommendation system will provide high performances in terms of both accuracy and diversity. The experimental results on the Movielens benchmark and on a real-world insurance dataset show the efficiency of our approach in terms of accuracy and diversity compared to state-of-the-art competitors.

An evolutionary scheme for decision tree construction

Classification is a central task in machine learning and data mining. Decision tree (DT) is one of the most popular learning models in data mining. The performance of a DT in a complex decision problem depends on the efficiency of its construction. However, obtaining the optimal DT is not a straightforward process. In this paper, we propose a new evolutionary meta-heuristic optimization based approach for identifying the best settings during the construction of a DT. We designed a genetic algorithm coupled with a multi-task objective function to pull out the optimal DT with the best parameters. This objective function is based on three main factors: (1) Precision over the test samples, (2) Trust in the construction and validation of a DT using the smallest possible training set and the largest possible testing set, and (3) Simplicity in terms of the size of the generated candidate DT, and the used set of attributes. We extensively evaluate our approach on 13 benchmark datasets and a fault diagnosis dataset. The results show that it outperforms classical DT construction methods in terms of accuracy and simplicity. They also show that the proposed approach outperforms Ant-Tree-Miner (an evolutionary DT construction approach), Naive Bayes and Support Vector Machine in terms of accuracy and F-measure.

A Novel Organizational Model for Real Time MAS: Towards a Formal Specification

In this paper we present our approach allowing the translation of Real Time Multi-Agents Systems (RT-MAS) organizational requirements described by extended AUML (Agent UML Language) diagrams into a formal specification written in Real Time Maude language (RT-Maude). In fact, the approach is an extension of our previous work [1] that consists in extending AUML diagrams (Temporal AUML organization use case diagram and Temporal AUML organization class diagram) by using stereotypes notions and meta-model organizations entities for taking into account RT-MAS specificities. Once elaborated, these different diagrams undergo a validation to assure inter-and intra model coherence. The formal and object oriented language RT-Maude, base on rewriting logic, supports formal specification and programming of concurrent systems. The main motivations of this work are: (1) formalizing the organizational requirements of RT-MAS by using RT-Maude language, and (2) integrating the validation of the coherence models, since the analysis phase.

Decision tree selection in an industrial machine fault diagnostics

Decision trees are widely used technique in data mining and classification fields. This method classifies objects following succession tests on their attributes. Its principal disadvantage is the choice of optimal model among the various existing trees types (Chaid, Cart,Id3..). Each tree has its specificities which make the choice justification difficult. In this work, decision tree choice validation is studied and the use of genetic algorithms is proposed. To pull out best tree, all models are generated and their performances measured on distinct training and validation sets. After that, various statistical tests are made. In this paper we propose the diagnosis accomplishment of an industrial ventilator(Fan) based an analysis-decision trees.

The Impact of Social Similarities and Event Detection on Ranking Retrieved Resources in Collaborative E-Learning Systems

Recently, the social web has recognized a real attention by E-learning community. This collaborative space gave students new opportunities to share their contents and receive immediate feedback from other networkers. For instance, in folksonomies, learners are able to tag useful resources within a highly visible space, which allow sharing ideas that gives a basis for discussion, and thus other students can benefit from those resources. Actually, social environments offer a unique opportunity to personalize search spaces. The objective of this work is to achieve this opportunity and thus personalize tag-based search in E-learning folksonomy by extract implicitly the semantics of learners’ tags. In this context, a social personalized ranking function is proposed; this function leverages the social aspect of folksonomy and events detection to estimate the relevance of given resources to a tag-based query issued by learners.

Recommendation in Collaborative E-Learning by Using Linked Open Data and Ant Colony Optimization

Social tagging activities allow the wide set of web users, especially learners, to add free annotations on educational resources to express their interests and automatically generate folksonomies. Folksonomies have been involved in a lot of recommendations approaches. Recently, supported by semantic web technologies, the Linked Open Data (LOD) allow to set up links between entities in the web to join information in a single global data space. This paper demonstrates how structured content accessible via LOD can be leveraged to support educational resources recommender in folksonomies and overcome the limited capabilities to analyze resources information. Another limitation of resources recommendation is the content overspecialization conducting in the incapacity to recommend relevant resources diverse from the ones that learner previously knows. To address these issues, we proposed to take advantage of the richness of the open and linked data graph of DBpedia and Ant Colony Optimization (ACO) to learn users’ behavior. The basic idea is to iteratively explore the RDF data graph to produce relevant and diverse recommendations as an alternative of going through the tedious phase of calculating similarity to attain the same goal. Using ant colony optimization, our system performs a search for the appropriate paths in the LOD graph and selects the best neighbors of an active learner to provide improved recommendations. In this paper, we show that ACO also in the problem of recommendation of novel diverse educational resources by exploring LOD is able to deliver good solutions.

An Evolutionary Scheme for Improving Recommender System Using Clustering

In user memory based collaborative filtering algorithm, recommendation quality depends strongly on the neighbors selection which is a high computation complexity task in large scale datasets. A common approach to overpass this limitation consists of clustering users into groups of similar profiles and restrict neighbors computation to the cluster that includes the target user. K-means is a popular clustering algorithms used widely for recommendation but initial seeds selection is still a hard complex step. In this paper a new genetic algorithm encoding is proposed as an alternative of k-means clustering. The initialization issue in the classical k-means is targeted by proposing a new formulation of the problem, to reduce the search space complexity affect as well as improving clustering quality. We have evaluated our results using different quality measures. The employed metrics include rating prediction evaluation computed using mean absolute error. Additionally, we employed both of precision and recall measures using different parameters. The obtained results have been compared against baseline techniques which proved a significant enhancement.