Risk prediction of product-harm events using rough sets and multiple classifier fusion:an experimental study of listed companies in China

With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm)

A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Shafer theory (DST). However, in cases with conflicting evidences, the DST may give counter-intuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it is applied for classifying polycrystalline Nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that that the proposed method improves the classification accuracy and outperforms the individual classifiers.Comment: arXiv admin note: text overlap with arXiv:2007.0878

Improving the performance of free-text keystroke dynamics authentication by fusion

Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR)

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models

Advances in pre-processing and model generation for mass spectrometric data analysis

The analysis of complex signals as obtained by mass spectrometric measurements is complicated and needs an appropriate representation of the data. Thereby the kind of preprocessing, feature extraction as well as the used similarity measure are of particular importance. Focusing on biomarker analysis and taking the functional nature of the data into account this task is even more complicated. A new mass spectrometry tailored data preprocessing is shown, discussed and analyzed in a clinical proteom study compared to a standard setting

Enhanced Prediction of Network Attacks Using Incomplete Data

For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is needed. This work presents an approach which seeks to combine a primary prediction model with a novel secondary confidence level model which provides a measurement of the confidence in a given attack prediction being made. The ability to accurately identify an attack and quantify the confidence level in the prediction could serve as the basis for a new generation of intrusion detection devices, devices that provide earlier and better alerts for administrators and allow more proactive response to events as they are occurring

A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network

This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states

Quantifying the reliability of fault classifiers

International audienceFault diagnostics problems can be formulated as classification tasks. Due to limited data and to uncertainty, classification algorithms are not perfectly accurate in practical applications. Maintenance decisions based on erroneous fault classifications result in inefficient resource allocations and/or operational disturbances. Thus, knowing the accuracy of classifiers is important to give confidence in the maintenance decisions. The average accuracy of a classifier on a test set of data patterns is often used as a measure of confidence in the performance of a specific classifier. However, the performance of a classifier can vary in different regions of the input data space. Several techniques have been proposed to quantify the reliability of a classifier at the level of individual classifications. Many of the proposed techniques are only applicable to specific classifiers, such as ensemble techniques and support vector machines. In this paper, we propose a meta approach based on the typicalness framework (Kolmogorov's concept of randomness), which is independent of the applied classifier. We apply the approach to a case of fault diagnosis in railway turnout systems and compare the results obtained with both extreme learning machines and echo state networks