Search Results for author:
Found 22 papers, 4 papers with code
TCE: A Test-Based Approach to Measuring Calibration Error
This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE).
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models.
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
We derive 6 research topics and 12 practical challenges for fraud detection from this operational model.
Mining Insights from Weakly-Structured Event Data
- An approach to detect and filter from event logs so-called chaotic activities, which are activities that cause process discovery methods to overgeneralize.
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)
Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process.
Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.
An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction
Data of sequential nature arise in many application domains in forms of, e. g. textual data, DNA sequences, and software execution traces.
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks
In this paper, we investigate the performance of several sequence prediction techniques on the prediction of future events of human behavior in a smart home, as well as the timestamps of those next events.
Alarm-Based Prescriptive Process Monitoring
Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof.
Mining Non-Redundant Local Process Models From Sequence Databases
Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database.
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques.
Generating Time-Based Label Refinements to Discover More Precise Process Models
Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.
Mining Process Model Descriptions of Daily Life through Event Abstraction
However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts.
The Imprecisions of Precision Measures in Process Mining
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction.
Interest-Driven Discovery of Local Process Models
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes.
Predictive Business Process Monitoring with LSTM Neural Networks
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Multivariate Time Series Forecasting
Predictive Process Monitoring
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Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.
On Generation of Time-based Label Refinements
Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing).
Event Abstraction for Process Mining using Supervised Learning Techniques
We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.
Log-based Evaluation of Label Splits for Process Models
We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.
Mining Local Process Models
The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.
