A dynamic semisupervised feedforward neural network clustering

Abstract

An efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

“…Because of these dynamic and nonstationary data, our system must use incremental learning methods. Incremental learning means learning by repeating the process of adding or deleting nodes without destroying outdated knowledge and patterns (Asadi, 2016). Online continuous data has multi-dimensions and noisy elements (Asadi, 2016).…”

“…So it can process both data and knowledge (Asadi, 2016). And not destroying old knowledge (Asadi, 2016). ODUFFNN stands for an online dynamic unsupervised feedforward neural network (Asadi, 2016).…”

“…Another example of neural networks prediction success is the prediction of breast cancer patients over time. This experiment has used ODUFFNN (online dynamic unsupervised feedforward neural network) in this model there are no feedbacks to previous nodes (Asadi, 2016), so we can achieve lower time complexity (Asadi, 2016). In ODUFFNN, we use incremental learning so it has sufficient results for a realistic environment (Asadi, 2016).…”

“…This experiment has used ODUFFNN (online dynamic unsupervised feedforward neural network) in this model there are no feedbacks to previous nodes (Asadi, 2016), so we can achieve lower time complexity (Asadi, 2016). In ODUFFNN, we use incremental learning so it has sufficient results for a realistic environment (Asadi, 2016). And ODUFFNN can automatically change and modify its structure, data nodes, connection and rules with each online input data dynamically, So this model has high flexibility (Asadi, 2016).…”

An efficient semisupervised feedforward neural network clustering

“…The review and investigation of current UFFNN clustering methods shows some sources of the mentioned problems that must be considered and solved (Asadi et al, 2013): Using random weights, thresholds, and parameters for controlling clustering tasks, initialization of the weights randomly results in the paradox of low accuracy and high training time. The clustering process is considerably slow because weights have to be updated in each epoch during learning.…”