Evaluation methods and decision theory for classification of streaming data with temporal dependence

Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data

Classifiers and text mining: application to a specific context

[Abstract]: The constant growth of social networks has not only brought us new ways of interacting with each other, but has also given way to a severe increase in negative behaviors: hate speech, racism, gender harassment, cyberbullying, etc. Manually trying to detect this kind of behaviours in millions of daily social media posts is out of the question. The solution lies in developing intelligent systems to automate such detection tasks. As the nature of these texts is completely subjective, this problem falls under the field of sentiment analysis, which aims to systematically identify and study affective states and subjective information in textual data using natural language processing techniques. In particular, this project is focused on the research of different machine learning techniques related to natural language processing, in order to automate and perform a reliable detection and classification of sexist-related behaviours in social media texts. We will tackle the task of adequately processing the extracted data from social media, as well as researching various text classification techniques and models that we will use to develop and evaluate a variety of classifiers.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2021/202

The effect of locality based learning on software defect prediction

Software defect prediction poses many problems during classification. A common solution used to improve software defect prediction is to train on similar, or local, data to the testing data. Prior work [12, 64] shows that locality improves the performance of classifiers. This approach has been commonly applied to the field of software defect prediction. In this thesis, we compare the performance of many classifiers, both locality based and non-locality based. We propose a novel classifier called Clump, with the goals of improving classification while providing an explanation as to how the decisions were reached. We also explore the effects of standard clustering and relevancy filtering algorithms.;Through experimentation, we show that locality does not improve classification performance when applied to software defect prediction. The performance of the algorithms is impacted more by the datasets used than by the algorithmic choices made. More research is needed to explore locality based learning and the impact of the datasets chosen

A New Similarity Measure for Document Classification and Text Mining

Accurate, efficient and fast processing of textual data and classification of electronic documents have become an important key factor in knowledge management and related businesses in today’s world. Text mining, information retrieval, and document classification systems have a strong positive impact on digital libraries and electronic content management, e-marketing, electronic archives, customer relationship management, decision support systems, copyright infringement, and plagiarism detection, which strictly affect economics, businesses, and organizations. In this study, we propose a new similarity measure that can be used with k-nearest neighbors (k-NN) and Rocchio algorithms, which are some of the well-known algorithms for document classification, information retrieval, and some other text mining purposes. We have tested our novel similarity measure with some structured textual data sets and we have compared the results with some other standard distance metrics and similarity measures such as Cosine similarity, Euclidean distance, and Pearson correlation coefficient. We have obtained some promising results, which show that this proposed similarity measure could be alternatively used within all suitable algorithms, methods, and models for text mining, document classification, and relevant knowledge management systems. Keywords: text mining, document classification, similarity measures, k-NN, Rocchio algorith

An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning

The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and prediction of large-scale data streams undergoing change in underlying class distributions (definitions) over time; yet currently, they often ignore significant foundational learning theory that has been developed in the domain of human learning. This shortfall leads to many deficiencies in the ability to organize existing knowledge and to retain relevant knowledge for long periods of time. In this work, we introduce a unique computer-learning algorithm for incremental knowledge acquisition using an ensemble of classifiers, Learn++.NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++.NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data environment. Under this architecture, we address the learning problem at both the learner and supervisor end, discussing and implementing three main approaches: knowledge weighting/organization, forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, supporting the organization technique proposed by Learn++.NSE as the most consistent performer during various drift scenarios, while also addressing the sheer difficulty in designing a system that strikes a balance between maintaining all knowledge and making decisions based only on relevant knowledge, especially in severe, unpredictable environments which are often encountered in the real-world

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques