Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.
Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages.
Can we train interpretable and accurate models, without timeless feature engineering?
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection.
In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network.
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects.