Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Modern Applied Statistics With S

The impact of class imbalance in classification performance metrics based on the binary confusion matrix

Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance .

Multi-Imbalance : An open-source software for multi-class imbalance learning

Supervised machine-learning algorithms are used to solve classification problems across the entire spectrum of computing platforms, from data centers to wearable devices, and place significant demand on their computational capabilities. In this paper, we propose scalable-effort classifiers, a new approach to optimizing the energy efficiency of supervised machine-learning classifiers. We observe that the inherent classification difficulty varies widely across inputs in real-world datasets; only a small fraction of the inputs truly require the full computational effort of the classifier, while the large majority can be classified correctly with very low effort. Yet, state-of-the-art classification algorithms expend equal effort on all inputs, irrespective of their difficulty. To address this inefficiency, we introduce the concept of scalable-effort classifiers, or classifiers that dynamically adjust their computational effort depending on the difficulty of the input data, while maintaining the same level of accuracy. Scalable effort classifiers are constructed by utilizing a chain of classifiers with increasing levels of complexity (and accuracy). Scalable effort execution is achieved by modulating the number of stages used for classifying a given input. Every stage in the chain contains an ensemble of biased classifiers, where each biased classifier is trained to detect a single class more accurately. The degree of consensus between the biased classifiers' outputs is used to decide whether classification can be terminated at the current stage or not. Our methodology thus allows us to transform any given classification algorithm into a scalable-effort chain. We build scalable-effort versions of 8 popular recognition applications using 3 different classification algorithms. Our experiments demonstrate that scalable-effort classifiers yield 2.79x reduction in average operations per input, which translates to 2.3x and 1.5x improvement in energy for hardware and software implementations, respectively.

/pdf/scalable-effort-classifiers-for-energy-efficient-machine-1a2k56u0b8.pdf

Scalable-effort classifiers for energy-efficient machine learning

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https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=4544&context=etd

A balanced approach to the multi-class imbalance problem

Visually Monitoring the 2008 Election

The short paper describes the major findings of the ISU Statistical Graphics working group on airline traffic in the USA. Flight volumes at major airports are increasing. Delays decreased after structural changes in 2002–2003 but have been increasing again since and delays build up during the day reaching a peak in the early evening hours. There is some hint of wind direction and strength affecting delays. This article has supplementary material online.

Delayed, Canceled, on Time, Boarding… Flying in the USA

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A balanced approach to the multi-class imbalance problem

Visually Monitoring the 2008 Election

Delayed, Canceled, on Time, Boarding… Flying in the USA