Data Mining - Concepts and Techniques.

Bidirectional LSTM with attention mechanism and convolutional layer for text classification

The aim of this study is to provide an overview the state-of-the-art elements of text classification. For this purpose, we first select and investigate the primary and recent studies and objectives in this field. Next, we examine the state-of-the-art elements of text classification. In the following steps, we qualitatively and quantitatively analyse the related works. Herein, we describe six baseline elements of text classification including data collection, data analysis for labelling, feature construction and weighing, feature selection and projection, training of a classification model, and solution evaluation. This study will help readers acquire the necessary information about these elements and their associated techniques. Thus, we believe that this study will assist other researchers and professionals to propose new studies in the field of text classification.

A recent overview of the state-of-the-art elements of text classification

The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject - appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.

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A Comprehensive Survey for Intelligent Spam Email Detection

A scheme is designed to achieve phase synchronization (PS) and antiphase synchronization (APS) for an n-dimensional hyperchaotic complex nonlinear system. For this scheme, we have used the idea of an active control technique based on Lyapunov stability analysis to determine analytically the complex control functions which are needed to achieve PS and APS. We applied this scheme, as an example, to study PS and APS of hyperchaotic attractors of two identical hyperchaotic complex Lorenz systems. These complex systems appear in many important fields of physics and engineering. Our scheme can also be applied to two different hyperchaotic complex systems, for which PS and APS have not been investigated, as far as we know, in the literature. Numerical results are plotted to show phases and amplitudes of these hyperchaotic attractors, thus demonstrating that PS and APS are achieved. The bifurcation diagrams are computed for a wide range of parameters of the system parameters and are found to be symmetrical about the horizontal axis for APS, while they lack any symmetry for PS.

Phase and antiphase synchronization of two identical hyperchaotic complex nonlinear systems

The rapid popularization of smartphones has contributed to the growth of online Instant Messaging and SMS usage as an alternative way of communication The increasing number of users, along with the trust they inherently have in their devices, makes such messages a propitious environment for spammers In fact, reports clearly indicate that volume of spam over Instant Messaging and SMS is dramatically increasing year by year It represents a challenging problem for traditional filtering methods nowadays, since such messages are usually fairly short and normally rife with slangs, idioms, symbols and acronyms that make even tokenization a difficult task In this scenario, this paper proposes and then evaluates a method to normalize and expand original short and messy text messages in order to acquire better attributes and enhance the classification performance The proposed text processing approach is based on lexicographic and semantic dictionaries along with state-of-the-art techniques for semantic analysis and context detection This technique is used to normalize terms and create new attributes in order to change and expand original text samples aiming to alleviate factors that can degrade the algorithms performance, such as redundancies and inconsistencies We have evaluated our approach with a public, real and non-encoded data-set along with several established machine learning methods Our experiments were diligently designed to ensure statistically sound results which indicate that the proposed text processing techniques can in fact enhance Instant Messaging and SMS spam filtering

Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering

We show experimental and numerical results of phase synchronization between the chaotic Chua circuit and a small sinusoidal perturbation. Experimental real-time phase synchronized states can be detected with oscilloscope visualization of the attractor, using specific sampling rates. Arnold tongues demonstrate robust phase synchronized states for perturbation frequencies close to the characteristic frequency of the unperturbed Chua.

Phase synchronization in the perturbed Chua circuit.

In many real-world applications data arrive continuously, in the form of streams. Such data can be used for the acquisition of knowledge by machine learning methods. In data streams learning, novelty detection is a relevant topic, which aims to identify the emergence of a new concept or a drift in the known concept in real time. Most approaches in the literature that focus on the novelty detection problem, make assumptions that limit the method usefulness. For instance, some methods are designed lying on the supposition that labeled data will be available at some time in the stream, while others restrict the proposed algorithm to one-class problems. Some recent approaches aim to overcome the limitations mentioned, considering multiclass problems and unlabeled datasets. In addition, there are also proposals that explore concepts of fuzzy set theory to add more flexibility to the learning process, although restricted to labeled datasets. In this paper, we propose a fuzzy multiclass novelty detector for data streams called FuzzND, as a fuzzy extension of the MINAS algorithm. Our algorithm generates a model based on fuzzy micro-clusters that provides flexible class boundaries. Allowing the identification of different types of novel information, i.e, novel classes, extension of classes or outliers more efficiently. Experiments show that our approach is promising in dealing with the changes in data streams and presents improvements in comparison to the non-fuzzy version.

A Fuzzy Multiclass Novelty Detector for Data Streams

In many real-world applications data arrive continuously, in the form of streams. Such data can be used for the acquisition of knowledge by machine learning methods. In data streams learning, novelty detection is a relevant topic, which aims to identify the emergence of a new concept or a drift in the known concept in real time. Most approaches in the literature that focus on the novelty detection problem, make assumptions that limit the method usefulness. For instance, some methods are designed lying on the supposition that labeled data will be available at some time in the stream, while others restrict the proposed algorithm to one-class problems. Some recent approaches aim to overcome the limitations mentioned, considering multiclass problems and unlabeled data streams. In addition, there are also proposals that explore concepts of fuzzy set theory to add more flexibility to the learning process, although restricted to labeled streams. In this paper, we propose a method for novelty detection in data streams called Possibilistic Fuzzy multiclass Novelty Detector for data streams (PFuzzND). Our methods generates models based on a proposed summarization structures named SPFMiC (Supervised Possibilistic Fuzzy Micro-Cluster), which provides flexible class boundaries, allowing the identification of different types of novel information, i.e, novel classes, extension of classes or outliers more efficiently. Experiments show that our approach is promising in dealing with the changes in data streams and presents improvements in comparison to the non-fuzzy version.

Possibilistic Approach For Novelty Detection In Data Streams

In many real-world applications, data arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. In data streams learning, classification problems aim to predict the true class of incoming instances in real time. While adhering to online learning strategies, in this paper we extend the On-Demand classification algorithm to include concepts of fuzzy sets theory as a way to make classification more flexible to stream changes. A set of experiments was conducted to evaluate the proposed method. Experiments show that our approach is promising in dealing with imbalanced data streams and presents benefits with relation to the non-fuzzy version.

Tiago Pinho da Silva

Papers

Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering

Phase synchronization in the perturbed Chua circuit.

A Fuzzy Multiclass Novelty Detector for Data Streams

Possibilistic Approach For Novelty Detection In Data Streams

A Fuzzy Variant for On-Demand Data Stream Classification