https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1657_2013-Lopez-INS.pdf

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysisis a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

Outlier Analysis

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

/pdf/smote-for-learning-from-imbalanced-data-progress-and-3slcu62yo6.pdf

SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high-and low-level vision classification tasks that exhibit imbalanced class distribution.

/pdf/learning-deep-representation-for-imbalanced-classification-60lnajwhc0.pdf

Learning Deep Representation for Imbalanced Classification

Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this paper, we focus on designing modifications to support vector machines (SVMs) to appropriately tackle the problem of class imbalance. We incorporate different ldquorebalancerdquo heuristics in SVM modeling, including cost-sensitive learning, and over- and undersampling. These SVM-based strategies are compared with various state-of-the-art approaches on a variety of data sets by using various metrics, including G-mean, area under the receiver operating characteristic curve, F-measure, and area under the precision/recall curve. We show that we are able to surpass or match the previously known best algorithms on each data set. In particular, of the four SVM variations considered in this paper, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency. GSVM-RU is effective, as it can minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. GSVM-RU is efficient by extracting much less support vectors and, hence, greatly speeding up SVM prediction.

/pdf/svms-modeling-for-highly-imbalanced-classification-2ry9lnuc0f.pdf

SVMs Modeling for Highly Imbalanced Classification

Methods and systems for operation upon one or more data processors for prioritizing transmission among a plurality of data streams based upon a classification associated with the data packets associated with each of the plurality of data streams, respectively. Systems and methods can operate to allocate bandwidth to priority data streams first and recursively allocate remaining bandwidth to lesser priority data streams based upon the priority associated with those respective lesser priority data streams.

Prioritizing network traffic

Users and network administrators need ways to filter email messages based primarily on the reputation of the sender. Unfortunately, conventional mechanisms for sender reputation--notably, IP blacklists--are cumbersome to maintain and evadable. This paper investigates ways to infer the reputation of an email sender based solely on network-level features, without looking at the contents of a message. First, we study first-order properties of network-level features that may help distinguish spammers from legitimate senders. We examine features that can be ascertained without ever looking at a packet's contents, such as the distance in IP space to other email senders or the geographic distance between sender and receiver. We derive features that are lightweight, since they do not require seeing a large amount of email from a single IP address and can be gleaned without looking at an email's contents--many such features are apparent from even a single packet. Second, we incorporate these features into a classification algorithm and evaluate the classifier's ability to automatically classify email senders as spammers or legitimate senders. We build an automated reputation engine, SNARE, based on these features using labeled data from a deployed commercial spam-filtering system. We demonstrate that SNARE can achieve comparable accuracy to existing static IP blacklists: about a 70%detection rate for less than a 0.3%false positive rate. Third, we show how SNARE can be integrated into existing blacklists, essentially as a first-pass filter.

/pdf/detecting-spammers-with-snare-spatio-temporal-network-level-2pqhkwfk7q.pdf

Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine

Methods and systems for operation upon one or more data processors for assigning a reputation to a messaging entity. A method can include receiving data that identifies one or more characteristics related to a messaging entity's communication. A reputation score is determined based upon the received identification data. The determined reputation score is indicative of reputation of the messaging entity. The determined reputation score is used in deciding what action is to be taken with respect to a communication associated with the messaging entity.

Systems and methods for classification of messaging entities

Methods and systems for assigning reputation to communications entities include collecting communications data from distributed agents, aggregating the communications data, analyzing the communications data and identifying relationships between communications entities based upon the communications data.

Sven Krasser

Papers

SVMs Modeling for Highly Imbalanced Classification

Prioritizing network traffic

Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine

Systems and methods for classification of messaging entities

Multi-Dimensional Reputation Scoring