Résumé: Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.

Résumé: This paper investigates feature selection methods based on hybrid architecture using feature selection algorithm called Adapted Fast Correlation Based Feature selection and Support Vector Machine Recursive Feature Elimination (AFCBF-SVMRFE). The AFCBF-SVMRFE has three stages and composed of SVMRFE embedded method with Correlation based Features Selection. The first stage is the relevance analysis, the second one is a redundancy analysis, and the third stage is a performance evaluation and features restoration stage. Experiments show that the proposed method tested on different classifiers: Support Vector Machine SVM and K nearest neighbors KNN provide a best accuracy on various dataset. The SVM classifier outperforms KNN classifier on these data. The AFCBF-SVMRFE outperforms FCBF multivariate filter, SVMRFE, Particle swarm optimization PSO and Artificial bees colony ABC.

Résumé: Automatic speaker verification (ASV) is a binary classification task. It consists of accepting or rejecting the claimed identity. ASV system has to decide whether a claimed speaker uttered a sentence. This paper proposes an algorithm called impostor random vector quantisation (IRVQ) based on multiples random codebook. IRVQ represents the impostor model also called universal background model (UBM) and we compare it to the second algorithm called partial impostor VQ (IVQ) for vector quantisation (VQ) in speaker verification. The present study demonstrates that the several random selected codebooks representing impostor models give better results and less half total error HTER than impostor IVQ method and baseline system. The performance of these models is evaluated on Arabic speaker verification dataset. However, this improvement also depends on the codebook size. The assumption concerning partitioning impostor acoustic spaces and choosing the best subspace to represent impostor model specific to each speaker is an efficient approach.

Résumé: The performance of speaker verification system degrades when the test segments are utterances of short duration, therefore, we investigate the use of model representing our target speaker with his close speaker and his own speech data. We propose to create a new Speaker Model who groups close speakers (CS) achieved with two clustering algorithms in Automatic Speaker Verification A.S.V. Intra and Inter speaker’s variability are two clustering algorithm used in voice module. We compare the traditional approach which uses one specific customer model (Maximum a Posteriori Adaptation) with the Close Speaker model (Customers Families).Close Speaker Model (CSM) applied only when speaker model is weak achieves 42% of equal error rate. The results demonstrate that the log likelihood of close speakers is greater than the likelihood of client speaker. The false alarm from client and CSM are closest and we are constrained to enhance speaker model.

Résumé: This work presents a medical decision support system which aims to help physicians in the diagnosis of a dangerous respiratory disease caused by tobacco and named: Chronic Obstructive Pulmonary Disease. The system is developed as a part of a project realized with the pneumology department of Dorban Hospital in Annaba (Algeria). In this paper the management of two issues is made. The first one is about the weighting of the clinical parameters on which the diagnosis is based. Seven versions of weight values are established and evaluated. The second issue is the prevention of the diagnosis process completion by the problem of missing data. To remedy this problem, an approach is proposed and evaluated with the combination of the seven versions of weight values. Results were quite satisfactory.

Résumé: This paper investigates Chaotic Restart Binary Particle Swarm PSO (ChResBPSO) algorithm applied to feature selection. In this study, to escape local optima of particle swarm PSO and solve the stagnation problem, we add new particles using prior information about current global best and its neighborhood. The solution adopted is to update the particles using N-Best previous particles, their neighbors and new random particles. The information on worst features is incorporated to direct the novel solutions to avoid them. Various chaotic systems replace the main parameters of PSO to find the best chaotic map. Experiments conducted on UCI data: hepatitis, breast cancer, colon cancer, DLBCL validate that chaotic PSO with anti-stagnation criterion outperforms the state of the art methods (BPSO), chaotic BPSO, artificial bee colony (ABC). The novel ChRes-BPSO enhances the final solution in term of accuracy and minimal number of features.

Résumé: This article proposes a Novel Search Equation of Artificial Bee Colony called NSABC. This study introduces new search equations for employed and onlooker bees involving more parameters for each food source (more features) and avoid local optima. NSABC outperforms various ABC variants and particle swarm optimization PSO in term of reduced size of feature and accuracy. Experimental results validate the efficiency of NSABC method on machine learning UCI data.

Résumé: This paper investigates Chaotic Restart Binary Particle Swarm PSO (ChResBPSO) algorithm applied to feature selection. In this study, to escape local optima of particle swarm PSO and solve the stagnation problem, we add new particles using prior information about current global best and its neighborhood. The solution adopted is to update the particles using N-Best previous particles, their neighbors and new random particles. The information on worst features is incorporated to direct the novel solutions to avoid them. Various chaotic systems replace the main parameters of PSO to find the best chaotic map.
Experiments conducted on UCI data: hepatitis, breast cancer, colon cancer, DLBCL validate that chaotic PSO with anti-stagnation criterion outperforms the state of the art methods (BPSO), chaotic BPSO, artificial bee colony (ABC). The novel ChResBPSO enhances the final solution in term of accuracy and minimal number of features.

Résumé: This paper investigates feature selection method using two hybrid approaches based on artificial Bee colony ABC with Particle Swarm PSO algorithm (ABC-PSO) and ABC with genetic algorithm (ABC-GA). To achieve balance between exploration and exploitation a novel improvement is integrated in ABC algorithm. In this work, particle swarm PSO contribute in ABC during employed bees, and GA mutation operators are applied in Onlooker phase and Scout phase. It has been found that the proposed method hybrid ABC-GA method is competitive than exiting methods (GA, PSO, ABC) for finding minimal number of features and classifying WDBC, colon, hepatitis, DLBCL, lung cancer dataset. Experimental results are carried out on UCI data repository and show the effectiveness of mutation operators in term of accuracy and particle swarm for less size of features.

Résumé: This paper investigates feature selection stage applied to medical classification of disease on datasets from UCI repository. Feature selection methods based on minimum Redundancy Maximum Relevance (mRMR) filter and Ficher score were applied, each of them select a subset of features then the selection criteria is used to get the initial features subset. The second stage Support vector machine recursive feature elimination is performed to have the final subset. Experiments show that the proposed method provide an accuracy of 99.89 % on hepatitis dataset and 97.81 % on Wisconcin Breast cancer dataset and outperforms MRMR and Support vector machine recursive feature elimination SVM-RFE methods, as well as other popular methods on UCI database, and select features that are relevant in discriminating cancer class (malign/benign).

Résumé: This paper presents a work consisting in realizing a decision support system based on the technique of case-base reasoning and dedicated to the diagnosis of a very dangerous pulmonary pathology: lung cancer. The system is realized for the oncology department of Ibn roch hospital of Annaba (Algeria) and will help young oncologist physicians in their activity by providing them with the experience of experts in the same domain. The principle issue in this work is the missing data in the system memory relating to the patient’s state. Indeed, missing values prevent the achievement of the diagnosis process. The problem is treated by proposing two statistical approaches in addition to re-evaluate in this new domain some ones which have been already proposed and evaluated in a previously domain. The validation is made on a base of 40 real cases collected from the archive of oncology department. Cases are collected as paper documents.