Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. Automated machine learning provides a solution to address this challenge. In this webinar, Justin Dickerson, General Manager of Global Finance for DataRobot, and Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

The final pay-per-view of the year is set for Sunday night with WWE Clash of Champions 2017. The night's biggest matches feature the likes of AJ Styles, Kevin Owens and Randy Orton. Four championships will be defended on the card. Below are predictions for every match on the WWE Clash of Champions card, which features wrestlers from "SmackDown Live." The Jinder Mahal experiment is over.

Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.

One of the toughest aspects of having epilepsy is not knowing when the next seizure will strike. A wearable warning system that detects pre-seizure brain activity and alerts people of its onset could alleviate some of that stress and make the disorder more manageable. To that end, IBM researchers say they have developed a portable chip that can do the job; they described their invention today in the Lancet's open access journal eBioMedicine. The scientists built the system on a mountain of brainwave data collected from epilepsy patients. The dataset, reported by a separate group in 2013, included over 16 years of continuous electroencephalography (EEG) recordings of brain activity, and thousands of seizures, from patients who had had electrodes surgically implanted in their brains.

Detecting outliers has become an increasing challenge in many areas, such as network intrusion detection, fraud detection, medical anomaly detection, and failure detection, as it was described by Chandola [1]. An outlier is basically a data that is significantly different from the normal behavior. In addition, several anomalies do not necessarily exhibit similar characteristics. Hence, detecting anomalies must be done by defining the normal behavior in the first place. Then, the deviation measured between an individual and the normal behavior gives good indications of anomalousness. However, as noticed in the same paper [1], defining a normal region that encompasses all the possible normal behaviors is sometimes really difficult. Moreover, an anomaly does not appear necessarily on all the explanatory variables, especially when the data is high-dimensional.

In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algorithms. In this paper, we investigate the applicability of stability selection to structured selection algorithms: the group lasso and the structured input-output lasso. We find that using stability selection often increases the power of both algorithms, but that the presence of complex structure reduces the reliability of error control under stability selection. We give strategies for setting tuning parameters to obtain a good model size under stability selection, and highlight its strengths and weaknesses compared to competing methods screen and clean and cross-validation. We give guidelines about when to use which error control method.

Anomaly detection is a very pervasive problem applicable to a variety of domains including network intrusion, fraud detection, and system failures. It is a crucial task in many applications because failure to detect anomalous activity could result in highly undesirable outcomes. For example, (i) detection of anomalous medical claims is important to identify fraud; (ii) detection of fraudulent credit card transactions is necessary to help prevent identity theft; and (iii) detection of abnormal network traffic is necessary to identify hacking. Many techniques have been developed for anomaly detection. These methods can be broadly classified into two categories: (i) rule-based systems, and (ii) statistical datadriven approaches. The rule-based systems are based on domain expertise and look for specific types of anomalies while the data-driven approaches look to identify anomalies by identifying statistically rare patterns. Examples of datadriven methods include parametric methods that assume a known family for the nominal (non-anomalous) distribution and nonparametric methods such as those using unsupervised or semi-supervised support vector machines (SVMs) [1], [2] or based on minimum volume set estimation [3], [4], [5]. The advantage of data-driven approaches over rule-based methods is that they can identify novel types of anomalies that are unknown to the domain expert.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater interest than the majority class instances in real-life applications. Recently, several techniques based on sampling methods (under-sampling of the majority class and over-sampling the minority class), cost-sensitive learning methods, and ensemble learning have been used in the literature for classifying imbalanced datasets. In this paper, we introduce a new clustering-based under-sampling approach with boosting (AdaBoost) algorithm, called CUSBoost, for effective imbalanced classification. The proposed algorithm provides an alternative to RUSBoost (random under-sampling with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost) algorithms. We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios. The experimental results show that the CUSBoost is a promising and effective approach for dealing with highly imbalanced datasets.

Semi-supervised learning (SSL) partially circumvents the high cost of labeling data by augmenting a small labeled dataset with a large and relatively cheap unlabeled dataset drawn from the same distribution. This paper offers a novel interpretation of two deep learning-based SSL approaches, ladder networks and virtual adversarial training (VAT), as applying distributional smoothing to their respective latent spaces. We propose a class of models that fuse these approaches. We achieve near-supervised accuracy with high consistency on the MNIST dataset using just 5 labels per class: our best model, ladder with layer-wise virtual adversarial noise (LVAN-LW), achieves 1.42% +/- 0.12 average error rate on the MNIST test set, in comparison with 1.62% +/- 0.65 reported for the ladder network. On adversarial examples generated with L2-normalized fast gradient method, LVAN-LW trained with 5 examples per class achieves average error rate 2.4% +/- 0.3 compared to 68.6% +/- 6.5 for the ladder network and 9.9% +/- 7.5 for VAT.