No abstract is provided for this article.
Datasets that have imbalanced class distributions pose a challenge for learning and classification algorithms. Imbalanced datasets exist in many domains, such as: fraud detection, sentiment analysis, churn prediction, and intrusion detection in computer networks. To solve the imbalance problem, three main approaches are typically used: data resampling, method adaptation and cost-sensitive learning; of these, data resampling, either oversampling the minority class instances or undersampling the majority class instances, is the most used approach. However, in most cases, when implementing these approaches, there is a trade-off between the predictive performance and the complexity. In this paper we introduce a fast, novel clustering-based undersampling technique for addressing binary-class imbalance problems, which demonstrates high predictive performance, while its time complexity is bound by the size of the minority class instances. During the training phase, the algorithm clusters the minority instances and selects a similar number of majority instances from each cluster. A specific classifier is then trained for each cluster. An unlabeled instance is classified as the majority class if it does not fit into any of the clusters. Otherwise, cluster-specific classifiers are used to return the instance's classification, and the results are weighted by the inverse-distance from the clusters. Our evaluation includes several state-of-the-art methods. We plot the Pareto frontier for various datasets, to consider both computational cost and predictive performance measures. Extensive sets of experiments demonstrate that only the suggested method is always found on the frontier.
XML transactions are used in many information systems to store data and interact with other systems. Abnormal transactions, the result of either an on-going cyber attack or the actions of a benign user, can potentially harm the interacting systems and therefore they are regarded as a threat. In this paper we address the problem of anomaly detection and localization in XML transactions using machine learning techniques. We present a new XML anomaly detection framework, XML-AD. Within this framework, an automatic method for extracting features from XML transactions was developed as well as a practical method for transforming XML features into vectors of fixed dimensionality. With these two methods in place, the XML-AD framework makes it possible to utilize general learning algorithms for anomaly detection. Central to the functioning of the framework is a novel multi-univariate anomaly detection algorithm, ADIFA. The framework was evaluated on four XML transactions datasets, captured from real information systems, in which it achieved over 89% true positive detection rate with less than a 0.2% false positive rate.
High throughput coherent optical transmitters are key components in future optical communication infrastructure. However, these transmitters are often distorted with the nonlinearity of their components. A potential approach for compensating nonlinearity is by applying digital pre-distortion methods based on the Volterra series or one of its derivatives. However, the Volterra series-based solution is complex to implement, difficult to scale, and its simplified versions may not yield the desired performance. Recently digital pre distortion solutions based on neural networks were proposed, which may benefit from the generality of neural networks and can be more easily scaled. These solutions are often based on non-standard neural network architectures which require complex neurons-based architectures or being based on indirect training approach which suffer from noise enhancement. In this article, a novel method for neural network-based pre-distortion with direct learning is proposed. The direct learning with neural network does not assume a specific transmitter model and does not suffer from noise enhancement. The method assumes standard neural network inference architecture and is applied to a coherent nonlinear optical transmitted with long-short-term memory neural network. The overall performance and complexity of the direct learning method is compared with the indirect approach and with the Volterra series-based solution, showing significant advantage in performance, especially in cases of severe nonlinearity and noise conditions.
Stacking is a general ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. Such an approach provides certain advantages: simplicity; performance that is similar to the best classifier; and the capability of combining classifiers induced by different inducers. The disadvantage of stacking is that on multiclass problems, stacking seems to perform worse than other meta-learning approaches. In this paper we present Troika, a new stacking method for improving ensemble classifiers. The new scheme is built from three layers of combining classifiers. The new method was tested on various datasets and the results indicate the superiority of the proposed method to other legacy ensemble schemes, Stacking and StackingC, especially when the classification task consists of more than two classes.
Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. The problem of intrusion detection can be solved using anomaly detection techniques. For instance, one is given a set of connection data belonging to different classes (normal activity, different attacks) and the aim is to construct a classifier that accurately classifies new unlabeled connections data. Clustering methods can be used to detect anomaly in data which might implies intrusion of a new type. This chapter gives a critical summary of anomaly detection research for intrusion detection. This chapter surveys a list of research projects that apply anomaly detection techniques to intrusion detection. Finally some directions for research are given.
This paper proposes several novel methods, based on machine learning, to detect malware in executable files without any need for preprocessing, such as unpacking or disassembling. The basic method (Mal-ID) is a new static (form-based) analysis methodology that uses common segment analysis in order to detect malware files. By using common segment analysis, Mal-ID is able to discard malware parts that originate from benign code. In addition, Mal-ID uses a new kind of feature, termed meta-feature, to better capture the properties of the analyzed segments. Rather than using the entire file, as is usually the case with machine learning based techniques, the new approach detects malware on the segment level. This study also introduces two Mal-ID extensions that improve the Mal-ID basic method in various aspects. We rigorously evaluated Mal-ID and its two extensions with more than ten performance measures, and compared them to the highly rated boosted decision tree method under identical settings. The evaluation demonstrated that Mal-ID and the two Mal-ID extensions outperformed the boosted decision tree method in almost all respects. In addition, the results indicated that by extracting meaningful features, it is sufficient to employ one simple detection rule for classifying executable files.
Time series classification is a field which has drawn much attention over the past decade. A new approach for classification of time series uses classification trees based on shapelets. A shapelet is a subsequence extracted from one of the time series in the dataset. A disadvantage of this approach is the time required for building the shapelet-based classification tree. The search for the best shapelet requires examining all subsequences of all lengths from all time series in the training set. A key goal of this work was to find an evaluation order of the shapelets space which enables fast convergence to an accurate model. The comparative analysis we conducted clearly indicates that a random evaluation order yields the best results. Our empirical analysis of the distribution of high-quality shapelets within the shapelets space provides insights into why randomized shapelets sampling is superior to alternative evaluation orders. We present an algorithm for randomized model generation for shapelet-based classification that converges extremely quickly to a model with surprisingly high accuracy after evaluating only an exceedingly small fraction of the shapelets space.
One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious. It is even harder when GPU hardware is unavailable. The increased availability of pre-trained and off-the-shelf word embeddings, models, and modules aim at easing the process of training large models and achieving a competitive performance. We explore the use of off-the-shelf BERT models and share the results of our experiments and compare their results to those of LSTM networks and more simple baselines. We show that the complexity and computational cost of BERT is not a guarantee for enhanced predictive performance in the classification tasks at hand.
Online social networks (OSNs), such as Facebook and Twitter, have become an integral part of our daily lives. There are hundreds of OSNs, and each offers particular services and functionalities. Recent studies show that many OSN users create accounts on multiple OSNs, using the same or different personal information. Collecting all the available data on an individual from several OSNs to fuse into a single profile can provide valuable information. In this paper, we introduce novel machine learning based methods for solving entity resolution (ER), a problem for matching user profiles across multiple OSNs. By using extracted features and supervised learning techniques, we developed classifiers which can perform entity matching between two profiles for the following scenarios: (a) matching users across two OSNs; (b) searching for a user by similar name; and (c) de-anonymizing a user's identity. The constructed classifiers were tested using data collected from two popular OSNs, Facebook and Xing. We then evaluated the classifiers' performances using measures such as true and false positive rates, accuracy, and the area under the receiver operator curve (AUC). The classification performance measured by AUC was quite remarkable, with an AUC of up to 0.982 and an accuracy of up to 95.9% in identifying user profiles across two OSNs.
No abstract is provided for this article.
Data mining methods can be used for discovering interesting patterns in manufacturing databases. These patterns can be used to improve manufacturing processes. However, data accumulated in manufacturing plants usually suffer from the 'Curse of Dimensionality', that is, relatively small number of records compared to large number of input features. As a result, conventional data mining methods may be inaccurate in these cases. This paper presents a new feature set decomposition approach that is based on genetic algorithm. For this purpose a new encoding schema is proposed and its properties are discussed. Moreover we examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of multiple oblivious trees classifiers. The new algorithm was tested on various real-world manufacturing data sets. The results obtained have been compared to other methods, indicating the superiority of the proposed algorithm.
Multi-level logic synthesis is a problem of immense practical significance, and is a key to developing circuits that optimize a number of parameters, such as depth, energy dissipation, reliability, etc. The problem can be defined as the task of taking a collection of components from which one wants to synthesize a circuit that optimizes a particular objective function. This problem is computationally hard, and there are very few automated approaches for its solution. To solve this problem we propose an algorithm, called Circuit-Decomposition Engine (CDE), that is based on learning decision trees, and uses a greedy approach for function learning. We empirically demonstrate that CDE, when given a library of different component types, can learn the function of Disjunctive Normal Form (DNF) Boolean representations and synthesize circuit structure using the input library. We compare the structure of the synthesized circuits with that of well-known circuits using a range of circuit similarity metrics.
No abstract is provided for this article.
No abstract is provided for this article.
Patients with chronic lymphoid leukemia (CLL), even in the Omicron era and after vaccination, suffer from persistent COVID-19 infection, higher complications, and mortality compared with the general population. In this study, we evaluated retrospectively the effectiveness of nirmatrelvir + ritonavir among 1080 patients with CLL who were infected with severe acute respiratory syndrome coronavirus 2. Nirmatrelvir administration was associated with a reduction in COVID-19–related hospitalization or death by day 35. Specifically, the rate of COVID-19–related hospitalization or death in the treated group compared with the untreated group was 4.8% (14 out of 292) vs 10.2% (75 out of 733), respectively. Moreover, we report a 69% relative risk reduction in COVID-19–related hospitalization or death in patients with CLL at the age of ≥65 years. Multivariate analysis indicates that patients aged >65 years, patients who received heavy treatment (>2 previous treatments), patients with recent hospitalizations, intravenous immunoglobulin (IVIG) treatment, and comorbidity had significant improvement outcomes after treatment with nirmatrelvir.
