1,626 research outputs found
Decision Stream: Cultivating Deep Decision Trees
Various modifications of decision trees have been extensively used during the
past years due to their high efficiency and interpretability. Tree node
splitting based on relevant feature selection is a key step of decision tree
learning, at the same time being their major shortcoming: the recursive nodes
partitioning leads to geometric reduction of data quantity in the leaf nodes,
which causes an excessive model complexity and data overfitting. In this paper,
we present a novel architecture - a Decision Stream, - aimed to overcome this
problem. Instead of building a tree structure during the learning process, we
propose merging nodes from different branches based on their similarity that is
estimated with two-sample test statistics, which leads to generation of a deep
directed acyclic graph of decision rules that can consist of hundreds of
levels. To evaluate the proposed solution, we test it on several common machine
learning problems - credit scoring, twitter sentiment analysis, aircraft flight
control, MNIST and CIFAR image classification, synthetic data classification
and regression. Our experimental results reveal that the proposed approach
significantly outperforms the standard decision tree learning methods on both
regression and classification tasks, yielding a prediction error decrease up to
35%
The Superiority of the Ensemble Classification Methods: A Comprehensive Review
The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners. Keywords: Ensemble, supervised learning, Ensemble model, AdaBoost, Bagging, Randomization, Boosting, Strong learner, Weak learner, classifier fusion, classifier selection, Classifier combination. DOI: 10.7176/JIEA/9-5-05 Publication date: August 31st 2019
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated learning setting where
multiple parties with different features about the same set of users jointly
train machine learning models without exposing their raw data or model
parameters. Motivated by the rapid growth in VFL research and real-world
applications, we provide a comprehensive review of the concept and algorithms
of VFL, as well as current advances and challenges in various aspects,
including effectiveness, efficiency, and privacy. We provide an exhaustive
categorization for VFL settings and privacy-preserving protocols and
comprehensively analyze the privacy attacks and defense strategies for each
protocol. In the end, we propose a unified framework, termed VFLow, which
considers the VFL problem under communication, computation, privacy, and
effectiveness constraints. Finally, we review the most recent advances in
industrial applications, highlighting open challenges and future directions for
VFL
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
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