Corpus ID: 29155233

Extreme Gradient Boosting and Behavioral Biometrics

  title={Extreme Gradient Boosting and Behavioral Biometrics},
  author={Benjamin Manning},
As insider hacks become more prevalent it is becoming more useful to identify valid users from the inside of a system rather than from the usual external entry points where exploits are used to gain entry. One of the main goals of this study was to ascertain how well Gradient Boosting could be used for prediction or, in this case, classification or identification of a specific user through the learning of HCIbased behavioral biometrics. If applicable, this procedure could be used to verify… Expand
Keystroke dynamics based biometric authentication: A hybrid classifier approach
The model proposed in this paper tries to overcome drawbacks and improves upon the current performance measures in the prevalent approaches that need to be addressed. Expand
An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments
Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However,Expand
Iterative Privileged Learning
Investigation of iterative privileged learning within the context of gradient boosted decision trees (GBDTs) demonstrates the benefits of studying privileged information in an iterative manner, as well as the effectiveness of the proposed algorithm. Expand
Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data
This work compares several ML and calibration algorithms for classifying tumor DNA methylation profiles and provides workflows for selecting, training and calibrating ML algorithms to generate well-calibrated multiclass probability estimates. Expand
PEA: Parallel electrocardiogram-based authentication for smart healthcare systems
This paper proposes a hybrid ECG feature extraction method that integrated fiducial- and non-fiducials-based features to extract more comprehensive ECG features and thereby improve the authentication stability. Expand
HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors
Metropolitan air pollution is a growing concern in both developing and developed countries. Fixed-station monitors, typically operated by governments, offer accurate but sparse data, and areExpand
Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms
Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status ofExpand
Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling and demonstrate the utility of a spectral subset of wavebands. Expand
Predicting human decisions with behavioral theories and machine learning
An open tournament for prediction of human choices in fundamental economic decision tasks is presented and it is suggested that integration of certain behavioral theories as features in machine learning systems provides the best predictions. Expand
Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA
It is demonstrated how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling, with XGBoost outperformed the other machine-learning approaches. Expand


Comparing anomaly-detection algorithms for keystroke dynamics
The objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. Expand
Stochastic gradient boosting
Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. TheExpand
Induction of Decision Trees
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail. Expand
Learning to rank using gradient descent
RankNet is introduced, an implementation of these ideas using a neural network to model the underlying ranking function, and test results on toy data and on data from a commercial internet search engine are presented. Expand
Ensemble Methods in Machine Learning
Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly. Expand
Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15)
  • 2000