For fraudulent claims, that would be a binary indicator for known fraudulent claims (no false positives) and known legitimate claims. Check to see if you have this data set and the time period for the data. You will need a representative sample of both types of claims. For a "suspicious claims" model, the dependent variable would be defined a little differently. You need a set of claims that were flagged as suspicious, investigated and found to be fraudulent.

Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.

The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously recorded from 24 participants while they were playing a computerised gambling task. Given the complexity of the data structure for this application, we extend simple functional historical models to models including random historical effects, factor-specific historical effects, and factor-specific random historical effects. Estimation is conducted by a component-wise gradient boosting algorithm, which scales well to large data sets and complex models.

In this paper, we address the problem of identifying spam users on Wikipedia and present our preliminary results. We formulate the problem as a binary classification task and propose a set of features based on user editing behavior to separate spammers from benign users. We tested our system on a new dataset we built consisting of 4.2K (half spam and half benign) users and 75.6K edits. Experimental results show that our approach reaches 80.8% classification accuracy and 0.88 mean average precision. We compared against ORES, the most recent tool developed by Wikimedia which assigns a damaging score to each edit, and we show that our system outperforms ORES in spam users detection. Moreover, by combining our features with ORES, classification accuracy increases to 82.1%. Additionally, we also show that our system performs well in a more realistic, unbalanced setting, that is, when spammers are greatly outnumbered by benign users, by achieving an AUROC of 0.84 (which increases to 0.86 when we combine with ORES).

Document classification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we're going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we'll have by the end of this tutorial is effective, and surprisingly accurate. Spam filtering is kind of like the "Hello world" of document classification. However, something to be aware of is that you aren't limited to two classes.

The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG. In this paper, we introduce PC with p-values (PC-p), a fast algorithm which robustly computes edge-specific p-values and then estimates and controls the FDR across the edges. PC-p specifically uses the p-values returned by many conditional independence tests to upper bound the p-values of more complex edge-specific hypothesis tests. The algorithm then estimates and controls the FDR using the bounded p-values and the Benjamini-Yekutieli FDR procedure. Modifications to the original PC algorithm also help PC-p accurately compute the upper bounds despite non-zero Type II error rates. Experiments show that PC-p yields more accurate FDR estimation and control across the edges in a variety of CPDAGs compared to alternative methods.

Preview: Canelo Alvarez (48–1–1, 34 KOs) and Julio Cesar Chavez Jr. (50–2–1, 32 KOs) meet in a non-title fight on Saturday night at T-Mobile Arena in Las Vegas. Alvarez enters the fight as the favorite at -600 compared to Chavez's 450. Both Mexican boxers weighed in at 164 pounds for the catchweight of 164.5 pounds. Alvarez is listed at 5-foot-9, with a reach of 70.5 inches. Chavez is 6-foot-1 and has a 73-inch reach.

In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method involves solving a convex, penalized Gaussian likelihood problem with a hierarchical group lasso penalty. The problem decomposes into independent subproblems which can be solved efficiently in parallel using first-order methods. Our method yields a sparse, symmetric, positive definite estimator of the precision matrix, encoding a Gaussian graphical model. We derive theoretical results not found in existing methods attaining this structure. In particular, our conditions for signed support recovery and estimation consistency rates in multiple norms are as mild as those in a regression problem. Empirical results show our method performing favorably compared to existing methods. We apply our method to genomic data to flexibly model linkage disequilibrium. Our method is also applied to improve the performance of discriminant analysis in sound recording classification.

With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in five departments at California State University Northridge (CSUN), based on a minimal number of lower division courses in each major. In addition, we use the detected clusters to identify hidden bottleneck courses.

In a Cinco de Mayo weekend battle between high-profile Mexican boxers, Julio Cesar Chavez Jr. seeks an upset over Canelo Alvarez. Alvarez, one of the top pound-for-pound boxers, enters Saturday's fight at T-Mobile Arena in Las Vegas as 1/6 favorite. The fight is expected to have heavy attention consider Alvarez's reputation and Chavez's famous father. The two boxers, fighting at 164 pounds, are arguably the most well-known active Mexican boxers and they are set to tangle on Mexico's most famous holiday. While Chavez has never fought on a Cinco de Mayo weekend, Alvarez defeated Amir Khan at about the same time last year and knocked out James Kirkland on May 9, 2015, and also held off Shane Mosley on Cinco de Mayo in 2012.