Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected works), Vol. 2

This second volume dedicated to Dezert-Smarandache Theory (DSmT) in Information Fusion brings in new fusion quantitative rules (such as the PCR1-6, where PCR5 for two sources does the most mathematically exact redistribution of conflicting masses to the non-empty sets in the fusion literature), qualitative fusion rules, and the Belief Conditioning Rule (BCR) which is different from the classical conditioning rule used by the fusion community working with the Mathematical Theory of Evidence. Other fusion rules are constructed based on T-norm and T-conorm (hence using fuzzy logic and fuzzy set in information fusion), or more general fusion rules based on N-norm and N-conorm (hence using neutrosophic logic and neutrosophic set in information fusion), and an attempt to unify the fusion rules and fusion theories. The known fusion rules are extended from the power set to the hyper-power set and comparison between rules are made on many examples. One defines the degree of intersection of two sets, degree of union of two sets, and degree of inclusion of two sets which all help in improving the all existing fusion rules as well as the credibility, plausibility, and communality functions. The book chapters are written by Frederic Dambreville, Milan Daniel, Jean Dezert, Pascal Djiknavorian, Dominic Grenier, Xinhan Huang, Pavlina Dimitrova Konstantinova, Xinde Li, Arnaud Martin, Christophe Osswald, Andrew Schumann, Tzvetan Atanasov Semerdjiev, Florentin Smarandache, Albena Tchamova, and Min Wang

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

Rating the Significance of Detected Network Events

Existing anomaly detection systems do not reliably produce accurate severity ratings for detected network events, which results in network operators wasting a large amount of time and effort in investigating false alarms. This project investigates the use of data fusion to combine evidence from multiple anomaly detection methods to produce a consistent and accurate representation of the severity of a network event. Four new detection methods were added to Netevmon, a network anomaly detection framework, and ground truth was collected from a latency training dataset to calculate the set of probabilities required for each of the five data fusion methods chosen for testing. The evaluation was performed against a second test dataset containing manually assigned severity scores for each event and the significance ratings produced by the fusion methods were compared against the assigned severity score to determine the accuracy of each data fusion method. The results of the evaluation showed that none of the data fusion methods achieved a desirable level of accuracy for practical deployment. However, Dempster-Shafer was the most promising of the fusion methods investigated due to correctly classifying more significant events than the other methods, albeit with a slightly higher false alarm rate. We conclude by suggesting some possible options for improving the accuracy of Dempster-Shafer that could be investigated as part of future work

Algorithms for sensor validation and multisensor fusion

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well