We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.

A project manager once told me that "any job worth doing is worth doing poorly." I understood exactly what she meant, and she knew that I would understand, especially when she preceded our conversation with these words: "I wouldn't say this to everyone, but I know you will understand what I mean." The message was clear to me because I was a perfectionist (and hopefully I have learned over the years to be less of a perfectionist thanks to my project manager's wise counsel). As a perfectionist, I would strive for 100% completion and perfection on every project, every analysis, and every report. It would take me longer than most people to finish the analysis and report, and my manager understood why.

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements -- 10 times more than any previous dataset -- and many times more instances of non-sarcastic statements, allowing for learning in regimes of both balanced and unbalanced labels. Each statement is furthermore self-annotated -- sarcasm is labeled by the author and not an independent annotator -- and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, compare it to previous related corpora, and provide baselines for the task of sarcasm detection.

Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noise in the extracted features and increasing the performance of prediction. Methods: In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Results and Conclusions: Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

The first "SmackDown Live" exclusive pay-per-view in two and a half months is set for Sunday night in Detroit. Eight matches are scheduled for WWE Hell in a Cell 2017, including the pre-show, and four titles will be on the line. Below are predictions for every match on the WWE Hell in a Cell card. Shane McMahon vs. Kevin Owens (Hell in a Cell Falls Count Anywhere Match) This has certainly been a feud worthy of a main-event match, with the story building slowly ever since Owens joined the blue brand in May. Anything can happen when there are no disqualifications, but the logical decision is to have the actual wrestler win. Shane had the biggest moment of WrestleMania 32 before losing to The Undertaker, and he held his own with AJ Styles before getting pinned at WrestleMania 33.

A well-calibrated estimator for the conditional probabilities should obey this equation. Once we have derived a statistical classifier, we need to validate it on some test data. This data should be different from that used to train the classifier, otherwise skill scores will be unduly optimistic. This is known as cross-validation. The confusion matrix expresses everything about the accuracy of a discrete classifier over a given database and you can use it to compose any possible skill score. Here, we are going to cover two that are rarely seen in the literature, but are nonetheless important for reasons that will become clear.

Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as "Super Learner". Super Learner uses V-fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of the Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details.

Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is challenging, and at present the implications for clinical care are largely hypothetical. This research demonstrates that, with well chosen cohorts (of bipolar disorder, borderline personality disorder, and control) and modern methods, it is possible to objectively learn to identify distinctive behaviour over short periods (20 reports) that effectively separate the cohorts. Participants with bipolar disorder or borderline personality disorder and healthy volunteers completed daily mood ratings using a bespoke smartphone app for up to a year. A signature-based machine learning model was used to classify participants on the basis of the interrelationship between the different mood items assessed and to predict subsequent mood. The signature methodology was significantly superior to earlier statistical approaches applied to this data in distinguishing the participant three groups, clearly placing 75% into their original groups on the basis of their reports. Subsequent mood ratings were correctly predicted with greater than 70% accuracy in all groups. Prediction of mood was most accurate in healthy volunteers (89-98%) compared to bipolar disorder (82-90%) and borderline personality disorder (70-78%).

Data science and statistics are not magic. They won't magically fix all of a company's problems. However, they are useful tools to help companies make more accurate decisions and automate repetitive work and choices that teams need to make. Machine learning and data science get referenced a lot when referring to natural language processing, imaging recognition and chat bots. However, they also can be applied to help managers make decisions, predict future revenues, segment markets, produce better content and diagnosis patients more effectively. Below, we are going to discuss some case examples of statistics and applied data science algorithms that can help your business and team produce more accurate results. This doesn't require complex hadoop clusters and cloud analytics. Just, let's get the basics going first! Before we jump to far down the rabbit hole of technology and hype!