Data Mining - Concepts and Techniques.

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

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Classifier chains for multi-label classification

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

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Classifier Chains for Multi-label Classification

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning for Anomaly Detection: A Survey.

Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. In this article, we present an overview of the evolution and current landscape of data and information quality research. We introduce a framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.

Overview and Framework for Data and Information Quality Research

The paper discusses outlier detection algorithms used in data mining systems. Basic approaches currently used for solving this problem are considered, and their advantages and disadvantages are discussed. A new outlier detection algorithm is suggested. It is based on methods of fuzzy set theory and the use of kernel functions and possesses a number of advantages compared to the existing methods. The performance of the algorithm suggested is studied by the example of the applied problem of anomaly detection arising in computer protection systems, the so-called intrusion detection systems.

Outlier Detection Algorithms in Data Mining Systems

In the paper, the most state-of-the-art methods of automatic text summarization, which build summaries in the form of generic extracts, are considered. The original text is represented in the form of a numerical matrix. Matrix columns correspond to text sentences, and each sentence is represented in the form of a vector in the term space. Further, latent semantic analysis is applied to the matrix obtained to construct sentences representation in the topic space. The dimensionality of the topic space is much less than the dimensionality of the initial term space. The choice of the most important sentences is carried out on the basis of sentences representation in the topic space. The number of important sentences is defined by the length of the demanded summary. This paper also presents a new generic text summarization method that uses nonnegative matrix factorization to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state-of-the-art methods on the DUC 2001 and DUC 2002 standard data sets.

Automatic text summarization using latent semantic analysis

Multi-label classification problem is a further generalization of traditional multi-class learning problem. In multi-label case the classes are not mutually exclusive and any sample may belong to several classes at the same time. Such problems occur in many important applications (in bioinformatics, text categorization, intrusion detection, etc.). In this paper we propose a new method for solving multi-label learning problem, based on paired comparisons approach. In this method each pair of possibly overlapping classes is separated by two probabilistic binary classifiers, which isolate the overlapping and non-overlapping areas. Then individual probabilities generated by binary classifiers are combined together to estimate final class probabilities fitting extended Bradley-Terry model with ties. Experimental performance evaluation on well-known multi-label benchmark datasets has demonstrated the outstanding accuracy results of the proposed method.

Paired Comparisons Method for Solving Multi-Label Learning Problem

In this paper, we employ machine learning methods to solve the problem of countering terrorism and extremism by using information from the Internet. This problem involves retrieving electronic messages, documents, and web resources that potentially contain information of terrorist or extremist nature, identifying the structure of user groups and online communities that disseminate this information, monitoring and modeling information flows in these communities, as well as assessing threats and predicting risks based on monitoring results. We propose some original language-independent algorithms for pattern-based information retrieval, thematic modeling, and prediction of message flow characteristics, as well as assessment and prediction of potential risk coming from members of online communities by using data on the structure of relations in these communities, which makes it possible to detect potentially dangerous users even without full access to the content they distribute, e.g., through private channels and chat rooms.

Machine Learning Methods for Detecting and Monitoring Extremist Information on the Internet

In this paper, we discuss the problem of anomaly detection in text data using convolutional neural network (CNN). Recently CNNs have become one of the most popular and powerful tools for various machine learning tasks. CNN’s main advantage is an ability to extract complicated hidden features from high dimensional data with complex structure. Usually CNNs are applied in supervised learning mode. On the other hand, unsupervised anomaly detection is an important problem in many applications, including computer security, behavioral analytics, etc. Since there is no specified target in unsupervised mode, traditional CNN’s objective functions cannot be used. In this paper, we develop a specific CNN architecture. It consists of one convolutional layer and one subsampling layer, we use RBF activation function and logarithmic loss function on the final layer. Minimization of the corresponding objective function helps us to calculate the location parameter of the features’ weights discovered on the last network layer. We use \(l_2\)-regularization to avoid degenerate solution. Proposed CNN has been tested on anomalies discovering in a stream of text documents modeled with well-known Enron dataset, where proposed method demonstrates better results in comparison with the traditional outlier detection methods based on one-class SVM and NMF.

Mikhail Petrovskiy

Papers

Outlier Detection Algorithms in Data Mining Systems

Automatic text summarization using latent semantic analysis

Paired Comparisons Method for Solving Multi-Label Learning Problem

Machine Learning Methods for Detecting and Monitoring Extremist Information on the Internet

Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data