Data Mining Using Surface and Deep Agents Based on Neural Networks

This paper presents an approach to data mining based on an architecture that uses two kinds of neural network-based agents: (i) an instantaneously-trained surface learning agent that quickly adapts to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two agents perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of a back propagation network for a variety of classification problems and found to be superior based on the RMS error criterion

PointMap: A real-time memory-based learning system with on-line and post-training pruning

Also published in the International Journal of Hybrid Intelligent Systems, Volume 1, January, 2004A memory-based learning system called PointMap is a simple and computationally efficient extension of Condensed Nearest Neighbor that allows the user to limit the number of exemplars stored during incremental learning. PointMap evaluates the information value of coding nodes during training, and uses this index to prune uninformative nodes either on-line or after training. These pruning methods allow the user to control both a priori code size and sensitivity to detail in the training data, as well as to determine the code size necessary for accurate performance on a given data set. Coding and pruning computations are local in space, with only the nearest coded neighbor available for comparison with the input; and in time, with only the current input available during coding. Pruning helps solve common problems of traditional memory-based learning systems: large memory requirements, their accompanying slow on-line computations, and sensitivity to noise. PointMap copes with the curse of dimensionality by considering multiple nearest neighbors during testing without increasing the complexity of the training process or the stored code. The performance of PointMap is compared to that of a group of sixteen nearest-neighbor systems on benchmark problems.This research was supported by grants from the Air Force Office of Scientific Research (AFOSR F49620-98-l-0108, F49620-0l-l-0397, and F49620-0l-l-0423) and the Office of Naval Research (ONR N00014-0l-l-0624)

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.Comment: 14 pages, 7 figures, 11 table

Comparative analysis of classification techniques for network fault management

Network troubleshooting is a significant process. Many studies were conducted about it. The first step in the troubleshooting procedures is represented in collecting information. It's collected in order to identify the problems. Syslog messages which are sent by almost all network devices include a massive amount of data that concern the network problems. Based on several studies, it was found that analyzing syslog data (which) can be a guideline for network problems and their causes. The detection of network problems can become more efficient if the detected problems have been classified based on the network layers. Classifying syslog data requires identifying the syslog messages that describe the network problems for each layer. It also requires taking into account the formats of syslog for vendors' devices. The present study aimed to propose a method for classifying the syslog messages which identify the network problem.This classification is conducted based on the network layers. This method uses data mining instrument to classify the syslog messages. The description part of the syslog message was used for carrying out the classification process.The relevant syslog messages were identified. The features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set was obtained from an educational network device. This dataset was used for the prediction stage. It was found that that LibSVM outperforms other classifiers in terms of the probability rate of the classified instances where it was in the range of 89.90%-32.80%. Furthermore, the validation results indicate that the probability rate of the correctly classified instances is >70%. © 2020 Turkiye Klinikleri. All rights reserved

Experimental fault characterization of a neural network

The effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size