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Found 13 papers, 4 papers with code
EMG subspace alignment and visualization for cross-subject hand gesture classification
Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces.
A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling
Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals.
A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection
The methodology supports the data scientist in the selection of an optimal feature subset by providing her with high-level information at three different levels: objectives, solutions, and individual features.
Between accurate prediction and poor decision making: the AI/ML gap
This paper argues that AI/ML community has taken so far a too unbalanced approach by devoting excessive attention to the estimation of the state (or target) probability to the detriment of accurate and reliable estimations of the utility.
Uplift vs. predictive modeling: a theoretical analysis
Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature.
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications.
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
Counterfactuals are central in causal human reasoning and the scientific discovery process.
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production
The dark face of digital commerce generalization is the increase of fraud attempts.
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process.
Feature selection in high-dimensional dataset using MapReduce
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems.
From dependency to causality: a machine learning approach
The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference.
Optimizing Component Combination in a Multi-Indexing Paragraph Retrieval System
We demonstrate a method to optimize the combination of distinct components in a paragraph retrieval system.
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
To attain such an objective, we performed a large scale comparison of these different strategies using a large experimental benchmark (namely the 111 series from the NN5 forecasting competition).
