Rule Extraction From Neuro-fuzzy System for Classification Using Feature Weights

2020 ◽  
Vol 9 (2) ◽  
pp. 59-79
Author(s):  
Heisnam Rohen Singh ◽  
Saroj Kr Biswas
Keyword(s):  
Feature Selection ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
False Positive Rate ◽  
Linguistic Features ◽  
Neuro Fuzzy ◽  
Quantitative Accuracy ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Improved Accuracy

Recent trends in data mining and machine learning focus on knowledge extraction and explanation, to make crucial decisions from data, but data is virtually enormous in size and mostly associated with noise. Neuro-fuzzy systems are most suitable for representing knowledge in a data-driven environment. Many neuro-fuzzy systems were proposed for feature selection and classification; however, they focus on quantitative (accuracy) than qualitative (transparency). Such neuro-fuzzy systems for feature selection and classification include Enhance Neuro-Fuzzy (ENF) and Adaptive Dynamic Clustering Neuro-Fuzzy (ADCNF). Here a neuro-fuzzy system is proposed for feature selection and classification with improved accuracy and transparency. The novelty of the proposed system lies in determining a significant number of linguistic features for each input and in suggesting a compelling order of classification rules using the importance of input feature and the certainty of the rules. The performance of the proposed system is tested with 8 benchmark datasets. 10-fold cross-validation is used to compare the accuracy of the systems. Other performance measures such as false positive rate, precision, recall, f-measure, Matthews correlation coefficient and Nauck's index are also used for comparing the systems. It is observed from the experimental results that the proposed system is superior to the existing neuro-fuzzy systems.

Epileptic Seizure Prediction Using Deep Transformer Model

2021 ◽  
Author(s):  
Abhijeet Bhattacharya ◽  
Tanmay Baweja ◽  
S. P. K. Karri
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
False Positive Rate ◽  
Superior Performance ◽  
Seizure Prediction ◽  
Advantages And Disadvantages ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Automated Screening ◽  
Transformer Model

The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning — this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

A Feature Selection Method for Improved Clonal Algorithm Towards Intrusion Detection

2016 ◽  
Vol 30 (05) ◽  
pp. 1659013 ◽  
Author(s):  
Chunyong Yin ◽  
Luyu Ma ◽  
Lu Feng
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
False Positive ◽  
False Positive Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Positive Rate ◽  
Better Than

Intrusion detection is a kind of security mechanism which is used to detect attacks and intrusion behaviors. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we proposed a feature selection method for improved clonal algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and clones them. Experimental results show that the feature selection algorithm is better than the traditional feature selection algorithm on the different classifiers, and it is shown that the final detection results are better than traditional clonal algorithm with 99.6% accuracy and 0.1% false positive rate.

scholarly journals Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features

2021 ◽  
Vol 13 (1) ◽  
pp. 1-6
Author(s):  
Dimaz Arno Prasetio ◽  
Kusrini Kusrini ◽  
M. Rudyanto Arief
Keyword(s):  
Feature Selection ◽  
False Positive ◽  
Good Accuracy ◽  
False Positive Rate ◽  
Attack Detection ◽  
Feature Modeling ◽  
Hybrid Features ◽  
Positive Rate ◽  
N Gram ◽  
Better Than

This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039%

scholarly journals IDSFS: A Signature Based Intrusion Detection System with High Pertinent Feature Selection Method

2019 ◽  
Vol 8 (2) ◽  
pp. 25-31
Author(s):  
S. Latha ◽  
Sinthu Janita Prakash
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Positive Rate ◽  
Frame Work ◽  
Pertinent Feature

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.

Feature selection for daily peak load forecasting using a neuro-fuzzy system

2014 ◽  
Vol 74 (7) ◽  
pp. 2321-2336 ◽  
Author(s):  
Sung-Yong Son ◽  
Sang-Hong Lee ◽  
Kyungyong Chung ◽  
Joon S. Lim
Keyword(s):  
Feature Selection ◽  
Fuzzy System ◽  
Peak Load ◽  
Load Forecasting ◽  
Neuro Fuzzy ◽  
Selection For

scholarly journals Wavelet Transforms based Fall Detection with Neuro-Fuzzy Systems based Feature Selection

2019 ◽  
Vol 8 (3) ◽  
pp. 7498-7502
Keyword(s):  
Feature Selection ◽  
Wavelet Transforms ◽  
Fuzzy Systems ◽  
Fall Detection ◽  
Wavelet Coefficients ◽  
Distribution Measurement ◽  
Neuro Fuzzy ◽  
Area Distribution ◽  
Minimum Number ◽  
Overlap Area

This study proposes a method to detect fall with minimum features selected by a non-overlap area distribution measurement (NADM) method. In preprocessing step, wavelet transforms were carried out to extract wavelet coefficients from dataset acquired by subjects. The NADM was used to select the minimum number of features from wavelet coefficients, and then 19 features were finally selected from the 33 features. The performance result of the fall detection was tested with 19 features, and then the sensitivity, accuracy, and specificity were shown to be 95%, 96.13%, and 97.25%, respectively

scholarly journals Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

2021 ◽  
Vol 14 (1) ◽  
pp. 123-129
Author(s):  
Yevgeniy Bodyanskiy ◽  
Anastasiia Deineko ◽  
Iryna Pliss ◽  
Olha Chala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hybrid Learning ◽  
Medical Diagnostics ◽  
Speed Of Information Processing ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Overlapping Classes

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

scholarly journals IProDNA-CapsNet: Identifying protein-DNA binding residues using capsule neural networks

2021 ◽  
Author(s):  
Binh Nguyen ◽  
QH Nguyen ◽  
GN Doan-Ngoc ◽  
TH Nguyen-Vo ◽  
S Rahardja
Keyword(s):  
Neural Networks ◽  
Dna Binding ◽  
False Positive Rate ◽  
Model Performance ◽  
Evaluation Metrics ◽  
Decision Threshold ◽  
Protein Dna Interactions ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Binding Residues

© 2019 The Author(s). Background: Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done. Results: Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%. Conclusions: According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

scholarly journals iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Binh P. Nguyen ◽  
Quang H. Nguyen ◽  
Giang-Nam Doan-Ngoc ◽  
Thanh-Hoang Nguyen-Vo ◽  
Susanto Rahardja
Keyword(s):  
Neural Networks ◽  
Dna Binding ◽  
False Positive Rate ◽  
Model Performance ◽  
Evaluation Metrics ◽  
Decision Threshold ◽  
Protein Dna Interactions ◽  
Positive Rate ◽  
Benchmark Datasets ◽  
Binding Residues

Abstract Background Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet – a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done. Results Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews’s correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%. Conclusions According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

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