A review on classification of imbalanced data for wireless sensor networks

© The Author(s) 2020. Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies

Concept coupling learning for improving concept lattice-based document retrieval

© 2017 Elsevier Ltd The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections

Efficient Learning Machines

Computer scienc

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective

Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for both short-term and long-term forecasting? How can a denoising mechanism enhance prediction accuracy? How can robust machine learning models be built with limited data? How can out-of-distribution traffic data be used to improve the generalizability of prediction models? Based on extensive experiments, we propose a novel traffic forecast/prediction framework and associated models that integrate outlier management and noise reduction strategies, outperforming traditional machine learning models. Additionally, we suggest a transfer learning-based framework combined with a data augmentation technique to provide robust solutions with smaller datasets. Lastly, we propose a hybrid model with signal decomposition techniques to enhance model generalization for out-of-distribution data samples. We also brought the issue of cyber threats as part of our forecast research, acknowledging their substantial influence on traffic unpredictability and forecasting challenges. Our thesis presents a detailed exploration of cyber-attack detection, employing methods that have been validated using multiple benchmark datasets. Initially, we incorporated ensemble feature selection with ensemble classification to improve DDoS (Distributed Denial-of-Service) attack detection accuracy with minimal false alarms. Our research further introduces a stacking ensemble framework for classifying diverse forms of cyber-attacks. Proceeding further, we proposed a weighted voting mechanism for Android malware detection to secure Mobile Cyber-Physical Systems, which integrates the mobility of various smart devices to exchange information between physical and cyber systems. Lastly, we employed Generative Adversarial Networks for generating flow-based DDoS attacks in Internet of Things environments. By considering the impact of cyber-attacks on traffic volume and their challenges to traffic prediction, our research attempts to bridge the gap between traffic forecasting and cyber security, enhancing proactive management of networks and contributing to resilient and secure internet infrastructure

Statistics in Education: Contributions to Teaching and Data Analysis

In this dissertation we present a compilation of the research conducted during the author’s doctoral program. In the first part, we discuss a case study regarding the impact of scholar-ships on student success at Oregon State University (OSU). Specifically, we look at the grad-uation and retention rates and aim to determine how the amount of financial aid provided to the students impacts these metrics, especially those who belong to vulnerable student groups. In the case study, we analyze data from first-time full-time (FTFT) freshmen that enrolled at OSU between 2011 and 2013. Using statistical models we first quantify and characterize the relationship between the amount of financial aid received by the students and the corre-sponding retention and graduation rates. As expected, the results show that the probabilities of retention and graduation increase as the amount of gift aid increases. We find that financial aid seems to have a greater impact on first year students. Further-more, we are able to characterize how these probabilities change when comparing students from different demographic groups. We find that these changes are more noticeable when looking at students in groups determined by Pell-eligibility, first-generation student status and financial need, even after accounting for metrics of student performance. We also discuss the problem of developing accurate models to predict the probability of retention and graduation based on the amount of financial aid offered to students and other relevant information. Such predictive models can be potentially used to guide policies and determine thresholds for scholarship amounts required to achieve the desired levels of grad-uation and retention rates at the university. Moreover, these models can be used to close achievement gaps for students from traditionally under-privileged backgrounds. We discuss the technical problem of binary classification with an imbalanced response vari-able and overlap in the feature space. These data difficulties present a challenge to the development of good predictive models for classification. The development of solutions to this problem is an area of active research in statistical and machine learning. In order to contribute a solution to this problem we first use simulations to characterize the impacts of imbalance and overlap in a variety of scenarios. The results of the simulation study are used in the creation of our novel algorithm for correcting the technical problem. Upon revisiting the predictive component of our practical problem on student success we found evidence of improved performance in certain cases where our algorithm was applied. The second part of the dissertation concerns the development and expansion of pedagogical practices for teaching statistical methods in higher education. Specifically, we discuss simple bootstrap methods that are often taught in introductory statistics courses. Bootstrapping and other resampling methods are progressively appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods. Some simple bootstrap-based inferential methods have more relaxed assumptions than their traditional counterparts possibly making it difficult to communicate their importance to students. Students and instructors of introductory statistics courses who are made aware of differences in the performance of these methods will better understand the importance of these assumptions. We detail some of the assumptions that the simple bootstrap relies on when used for uncertainty quantification and hypothesis testing. We emphasize the importance of these assumptions by using simulations to investigate the performance of these methods when they are or are not met. We also discuss software options for introducing undergraduate students to these bootstrap methods including the newly developed R package bootEd. The individual parts of this dissertation fall under the unifying theme of statistics in educa-tion. The results of our case study and our novel algorithm contribute to the use of statistics in the education sector. Meanwhile our pedagogical research on the bootstrap contributes to the teaching of statistics in the education sector. The ideas presented in this dissertation can, however, be extended to improve the teaching of subjects other than statistics and the analysis of data generated outside of educational settings. This research could also moti-vate future efforts to increase the functionality of institutions of education, which are quite foundational to a progressive and ethical society

Reprezentacije i metrike za mašinsko učenje i analizu podataka velikih dimenzija

In the current information age, massive amounts of data are gathered, at a rate prohibiting their effective structuring, analysis, and conversion into useful knowledge. This information overload is manifested both in large numbers of data objects recorded in data sets, and large numbers of attributes, also known as high dimensionality. This dis-sertation deals with problems originating from high dimensionality of data representation, referred to as the “curse of dimensionality,” in the context of machine learning, data mining, and information retrieval. The described research follows two angles: studying the behavior of (dis)similarity metrics with increasing dimensionality, and exploring feature-selection methods, primarily with regard to document representation schemes for text classification. The main results of the dissertation, relevant to the first research angle, include theoretical insights into the concentration behavior of cosine similarity, and a detailed analysis of the phenomenon of hubness, which refers to the tendency of some points in a data set to become hubs by being in-cluded in unexpectedly many k-nearest neighbor lists of other points. The mechanisms behind the phenomenon are studied in detail, both from a theoretical and empirical perspective, linking hubness with the (intrinsic) dimensionality of data, describing its interaction with the cluster structure of data and the information provided by class la-bels, and demonstrating the interplay of the phenomenon and well known algorithms for classification, semi-supervised learning, clustering, and outlier detection, with special consideration being given to time-series classification and information retrieval. Results pertaining to the second research angle include quantification of the interaction between various transformations of high-dimensional document representations, and feature selection, in the context of text classification.U tekućem „informatičkom dobu“, masivne količine podataka se sakupljaju brzinom koja ne dozvoljava njihovo efektivno strukturiranje, analizu, i pretvaranje u korisno znanje. Ovo zasićenje informacijama se manifestuje kako kroz veliki broj objekata uključenih u skupove podataka, tako i kroz veliki broj atributa, takođe poznat kao velika dimenzionalnost. Disertacija se bavi problemima koji proizilaze iz velike dimenzionalnosti reprezentacije podataka, često nazivanim „prokletstvom dimenzionalnosti“, u kontekstu mašinskog učenja, data mining-a i information retrieval-a. Opisana istraživanja prate dva pravca: izučavanje ponašanja metrika (ne)sličnosti u odnosu na rastuću dimenzionalnost, i proučavanje metoda odabira atributa, prvenstveno u interakciji sa tehnikama reprezentacije dokumenata za klasifikaciju teksta. Centralni rezultati disertacije, relevantni za prvi pravac istraživanja, uključuju teorijske uvide u fenomen koncentracije kosinusne mere sličnosti, i detaljnu analizu fenomena habovitosti koji se odnosi na tendenciju nekih tačaka u skupu podataka da postanu habovi tako što bivaju uvrštene u neočekivano mnogo lista k najbližih suseda ostalih tačaka. Mehanizmi koji pokreću fenomen detaljno su proučeni, kako iz teorijske tako i iz empirijske perspektive. Habovitost je povezana sa (latentnom) dimenzionalnošću podataka, opisana je njena interakcija sa strukturom klastera u podacima i informacijama koje pružaju oznake klasa, i demonstriran je njen efekat na poznate algoritme za klasifikaciju, semi-supervizirano učenje, klastering i detekciju outlier-a, sa posebnim osvrtom na klasifikaciju vremenskih serija i information retrieval. Rezultati koji se odnose na drugi pravac istraživanja uključuju kvantifikaciju interakcije između različitih transformacija višedimenzionalnih reprezentacija dokumenata i odabira atributa, u kontekstu klasifikacije teksta

Reprezentacije i metrike za mašinsko učenje i analizu podataka velikih dimenzija

In the current information age, massive amounts of data are gathered, at a rate prohibiting their effective structuring, analysis, and conversion into useful knowledge. This information overload is manifested both in large numbers of data objects recorded in data sets, and large numbers of attributes, also known as high dimensionality. This dis-sertation deals with problems originating from high dimensionality of data representation, referred to as the “curse of dimensionality,” in the context of machine learning, data mining, and information retrieval. The described research follows two angles: studying the behavior of (dis)similarity metrics with increasing dimensionality, and exploring feature-selection methods, primarily with regard to document representation schemes for text classification. The main results of the dissertation, relevant to the first research angle, include theoretical insights into the concentration behavior of cosine similarity, and a detailed analysis of the phenomenon of hubness, which refers to the tendency of some points in a data set to become hubs by being in-cluded in unexpectedly many k-nearest neighbor lists of other points. The mechanisms behind the phenomenon are studied in detail, both from a theoretical and empirical perspective, linking hubness with the (intrinsic) dimensionality of data, describing its interaction with the cluster structure of data and the information provided by class la-bels, and demonstrating the interplay of the phenomenon and well known algorithms for classification, semi-supervised learning, clustering, and outlier detection, with special consideration being given to time-series classification and information retrieval. Results pertaining to the second research angle include quantification of the interaction between various transformations of high-dimensional document representations, and feature selection, in the context of text classification.U tekućem „informatičkom dobu“, masivne količine podataka se sakupljaju brzinom koja ne dozvoljava njihovo efektivno strukturiranje, analizu, i pretvaranje u korisno znanje. Ovo zasićenje informacijama se manifestuje kako kroz veliki broj objekata uključenih u skupove podataka, tako i kroz veliki broj atributa, takođe poznat kao velika dimenzionalnost. Disertacija se bavi problemima koji proizilaze iz velike dimenzionalnosti reprezentacije podataka, često nazivanim „prokletstvom dimenzionalnosti“, u kontekstu mašinskog učenja, data mining-a i information retrieval-a. Opisana istraživanja prate dva pravca: izučavanje ponašanja metrika (ne)sličnosti u odnosu na rastuću dimenzionalnost, i proučavanje metoda odabira atributa, prvenstveno u interakciji sa tehnikama reprezentacije dokumenata za klasifikaciju teksta. Centralni rezultati disertacije, relevantni za prvi pravac istraživanja, uključuju teorijske uvide u fenomen koncentracije kosinusne mere sličnosti, i detaljnu analizu fenomena habovitosti koji se odnosi na tendenciju nekih tačaka u skupu podataka da postanu habovi tako što bivaju uvrštene u neočekivano mnogo lista k najbližih suseda ostalih tačaka. Mehanizmi koji pokreću fenomen detaljno su proučeni, kako iz teorijske tako i iz empirijske perspektive. Habovitost je povezana sa (latentnom) dimenzionalnošću podataka, opisana je njena interakcija sa strukturom klastera u podacima i informacijama koje pružaju oznake klasa, i demonstriran je njen efekat na poznate algoritme za klasifikaciju, semi-supervizirano učenje, klastering i detekciju outlier-a, sa posebnim osvrtom na klasifikaciju vremenskih serija i information retrieval. Rezultati koji se odnose na drugi pravac istraživanja uključuju kvantifikaciju interakcije između različitih transformacija višedimenzionalnih reprezentacija dokumenata i odabira atributa, u kontekstu klasifikacije teksta

Power system stability scanning and security assessment using machine learning

Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may involve large amount of scenarios and the existing planning tools may no longer suitable. Other than the raised future grids’ planning issues, operation of future grids using conventional tools is also challenged by the new features of future grids such as intermittent generation, demand response and fast responding power electronic plants which lead to much more diverse operation conditions compared to the existing networks. Among all operation issues, monitoring stability as well as security of a power system and action with deliberated preventive or remedial adjustment is of vital important. On- line Dynamic Security Assessment (DSA) can evaluate security of a power system almost instantly when current or imminent operation conditions are supplied. The focus of this dissertation are, for future grid planning, to develop a framework using Machine Learning (ML) to effectively assess the security of future grids by analyzing a large amount of the scenarios; for future grids operation, to propose approaches to address technique issues brought by future grids’ diverse operation conditions using ML techniques. Unsupervised learning, supervised learning and semi-supervised learning techniques are utilized in a set of proposed planning and operation security assessment tools

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Coupled fuzzy k-nearest neighbors classification of imbalanced non-IID categorical data

© 2014 IEEE. Mining imbalanced data has recently received increasing attention due to its challenge and wide applications in the real world. Most of the existing work focuses on numerical data by manipulating the data structure which essentially changes the data characteristics or developing new distance or similarity measures which are designed for data with the so-called IID assumption, namely data is independent and identically distributed. This is not consistent with the real-life data and business needs, which request to fully respect the data structure and coupling relationships embedded in data objects, features and feature values. In this paper, we propose a novel coupled fuzzy similarity-based classification approach to cater for the difference between classes by a fuzzy membership and the couplings by coupled object similarity, and incorporate them into the most popular classifier: kNN to form a coupled fuzzy kNN (ie. CF-kNN). We test the approach on 14 categorical data sets compared to several kNN variants and classic classifiers including C4.5 and NaiveBayes. The experimental results show that CF-kNN outperforms the baselines, and those classifiers incorporated with the proposed coupled fuzzy similarity perform better than their original editions