In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning b... more In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning by using a Growing Probabilistic Neural Network (GPNN) which combines clustering and classification. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of neurons. A new neuron is inserted when new data are not represented by existing neurons. For the Self-Training strategy, we chose the Support Vector Machines (SVM) as classifier because the SVMs are a powerful machine learning technique based on the principle of structural risk minimization. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is propose... more In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is proposed. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of nodes. A new neuron is inserted when new data are not represented by existing neurons. In training step, both supervised and unsupervised learning are used. The training dataset contains few samples with class labels and several unlabeled ones. The Support Vector Machines (SVM) operates in the training step to assess the INNCC classification result. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classifica... more Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classification de données bruitées en utilisant les réseaux de neurones. L'approche proposée est basée sur un réseau de neurones auto-organisé et incrémental constitué de deux couches. La construction des deux couches permet de représenter la structure topologique des données non étiquetées en ligne, de calculer le nombre de groupes et de donner les prototypes typiques de chaque cluster sans conditions préalables telles que le nombre de neurones. Pour confirmer l'efficacité de l'approche d'apprentissage proposée, nous présentons une série d'expériences avec des ensembles de données artificiels et réels.
Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classifica... more Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classification de données bruitées en utilisant les réseaux de neurones. L'approche proposée est basée sur un réseau de neurones auto-organisé et incrémental constitué de deux couches. La construction des deux couches permet de représenter la structure topologique des données non étiquetées en ligne, de calculer le nombre de groupes et de donner les prototypes typiques de chaque cluster sans conditions préalables telles que le nombre de neurones. Pour confirmer l'efficacité de l'approche d'apprentissage proposée, nous présentons une série d'expériences avec des ensembles de données artificiels et réels.
Engineering Applications of Artificial Intelligence, 2015
In this paper a new soft subspace clustering algorithm is proposed. It is an iterative algorithm ... more In this paper a new soft subspace clustering algorithm is proposed. It is an iterative algorithm based on the minimization of a new objective function. The classification approach is developed by acting at three essential points. The first one is related to an initialization step; we suggest to use a multi-class support vector machine (SVM) for improving the initial classification parameters. The second point is based on the new objective function. It is formed by a separation term and compactness ones. The density of clusters is introduced in the last term to yield different cluster shapes. The third and the most important point consists in an active learning with SVM incorporated in the classification process. It allows a good estimation of the centers and the membership degrees and a speed convergence of the proposed algorithm. The developed approach has been tested to classify different synthetic datasets and real images databases. Several indices of performance have been used to demonstrate the superiority of the proposed method. Experimental results have corroborated the effectiveness of the proposed method in terms of good quality and optimized runtime.
In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is propose... more In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is proposed. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of nodes. A new neuron is inserted when new data are not represented by existing neurons. In training step, both supervised and unsupervised learning are used. The training dataset contains few samples with class labels and several unlabeled ones. The Support Vector Machines (SVM) operates in the training step to assess the INNCC classification result. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
2015 12th International Symposium on Programming and Systems (ISPS), 2015
In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning b... more In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning by using a Growing Probabilistic Neural Network (GPNN) which combines clustering and classification. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of neurons. A new neuron is inserted when new data are not represented by existing neurons. For the Self-Training strategy, we chose the Support Vector Machines (SVM) as classifier because the SVMs are a powerful machine learning technique based on the principle of structural risk minimization. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
This paper presents a novel unsupervised and incremental learning technique for data clustering t... more This paper presents a novel unsupervised and incremental learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, reports the reasonable number of clusters and gives typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes. To confirm the efficiency of the proposed learning mechanism, we present a set of experiments with artificial and real world data sets.
In classification task, kernel functions are used to make possible to partition data that are lin... more In classification task, kernel functions are used to make possible to partition data that are linearly non-separable. In this paper, a Particle Swarm Optimization (PSO) is used to obtain optimal cluster centres, their weights features vectors and a kernel parameter by optimizing a cluster validity index. A comparative study has been conducted on synthetic and real dataset. The efficiency of the proposed method has been proven by the obtained results.
In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning b... more In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning by using a Growing Probabilistic Neural Network (GPNN) which combines clustering and classification. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of neurons. A new neuron is inserted when new data are not represented by existing neurons. For the Self-Training strategy, we chose the Support Vector Machines (SVM) as classifier because the SVMs are a powerful machine learning technique based on the principle of structural risk minimization. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is propose... more In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is proposed. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of nodes. A new neuron is inserted when new data are not represented by existing neurons. In training step, both supervised and unsupervised learning are used. The training dataset contains few samples with class labels and several unlabeled ones. The Support Vector Machines (SVM) operates in the training step to assess the INNCC classification result. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classifica... more Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classification de données bruitées en utilisant les réseaux de neurones. L'approche proposée est basée sur un réseau de neurones auto-organisé et incrémental constitué de deux couches. La construction des deux couches permet de représenter la structure topologique des données non étiquetées en ligne, de calculer le nombre de groupes et de donner les prototypes typiques de chaque cluster sans conditions préalables telles que le nombre de neurones. Pour confirmer l'efficacité de l'approche d'apprentissage proposée, nous présentons une série d'expériences avec des ensembles de données artificiels et réels.
Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classifica... more Cet article présente une approche d'apprentissage non supervisé et incrémental pour la classification de données bruitées en utilisant les réseaux de neurones. L'approche proposée est basée sur un réseau de neurones auto-organisé et incrémental constitué de deux couches. La construction des deux couches permet de représenter la structure topologique des données non étiquetées en ligne, de calculer le nombre de groupes et de donner les prototypes typiques de chaque cluster sans conditions préalables telles que le nombre de neurones. Pour confirmer l'efficacité de l'approche d'apprentissage proposée, nous présentons une série d'expériences avec des ensembles de données artificiels et réels.
Engineering Applications of Artificial Intelligence, 2015
In this paper a new soft subspace clustering algorithm is proposed. It is an iterative algorithm ... more In this paper a new soft subspace clustering algorithm is proposed. It is an iterative algorithm based on the minimization of a new objective function. The classification approach is developed by acting at three essential points. The first one is related to an initialization step; we suggest to use a multi-class support vector machine (SVM) for improving the initial classification parameters. The second point is based on the new objective function. It is formed by a separation term and compactness ones. The density of clusters is introduced in the last term to yield different cluster shapes. The third and the most important point consists in an active learning with SVM incorporated in the classification process. It allows a good estimation of the centers and the membership degrees and a speed convergence of the proposed algorithm. The developed approach has been tested to classify different synthetic datasets and real images databases. Several indices of performance have been used to demonstrate the superiority of the proposed method. Experimental results have corroborated the effectiveness of the proposed method in terms of good quality and optimized runtime.
In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is propose... more In this paper, an Incremental Neural Network for Classification and Clustering (INNCC) is proposed. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of nodes. A new neuron is inserted when new data are not represented by existing neurons. In training step, both supervised and unsupervised learning are used. The training dataset contains few samples with class labels and several unlabeled ones. The Support Vector Machines (SVM) operates in the training step to assess the INNCC classification result. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
2015 12th International Symposium on Programming and Systems (ISPS), 2015
In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning b... more In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning by using a Growing Probabilistic Neural Network (GPNN) which combines clustering and classification. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of neurons. A new neuron is inserted when new data are not represented by existing neurons. For the Self-Training strategy, we chose the Support Vector Machines (SVM) as classifier because the SVMs are a powerful machine learning technique based on the principle of structural risk minimization. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.
This paper presents a novel unsupervised and incremental learning technique for data clustering t... more This paper presents a novel unsupervised and incremental learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, reports the reasonable number of clusters and gives typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes. To confirm the efficiency of the proposed learning mechanism, we present a set of experiments with artificial and real world data sets.
In classification task, kernel functions are used to make possible to partition data that are lin... more In classification task, kernel functions are used to make possible to partition data that are linearly non-separable. In this paper, a Particle Swarm Optimization (PSO) is used to obtain optimal cluster centres, their weights features vectors and a kernel parameter by optimizing a cluster validity index. A comparative study has been conducted on synthetic and real dataset. The efficiency of the proposed method has been proven by the obtained results.
Uploads
Papers by amel hebboul