Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

Abstract

Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

“…The confusion matrix is used to campout the classification accuracy of the MLP1 model, as illustrated in Figure 5. The MLP1 model achieves the best accuracy of 95.62% using (1) [25][26][27][28][29][30][31] after 100 epochs.…”

Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization

“…The reason is that the testing captures the accuracy of predicting the location of the testing dataset which is composed of different records, however, it does not include an actual records generated from a traversed scenario. In order to test the algorithm based on given scenarios we adopt the simulator that is presented in [24]. This simulator allows providing a whole trajectory and it generates its corresponding time series of records from the dataset.…”

“…In this study, the authors applied the concept to Wi-Fi navigation and showed good performance improvement in the context of feature adaptability of Wi-Fi positioning system to preserve the knowledge in its neural network. The work conducted in [24,25] describes a novel type of extreme learning machine, using external memory and transfer learning; ITM-OSELM. In this study, the authors applied the concept to Wi-Fi localization and showed good performance improvement in the context of cyclic dynamic and feature adaptability of Wi-Fi navigation.…”

Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

“…This method calculates the time to process large amounts of data; however, setting a baseline that can be effectively distinguished is difficult. Al-Khaleefa et al [17] simulated the operation of the human brain, trained the weights of intermediate nodes with infinite term memory, and classified the packets into normal or abnormal patterns. This method is complex and time-consuming; security administrators cannot obtain clear information of the attacks through the trained weightings of nodes.…”