Ensemble Learning

199 papers with code • 1 benchmarks • 3 datasets

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Use these libraries to find Ensemble Learning models and implementations

Most implemented papers

Predicting the direction of stock market prices using random forest

jmartinezheras/reproduce-stock-market-direction-random-forests 29 Apr 2016

In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning.

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

namisan/mt-dnn 20 Apr 2019

This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks.

DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models

boun-tabi/debiaseddta-reproduce 4 Jul 2021

Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.

Gossip Learning with Linear Models on Fully Distributed Data

tribler/trustchain-superapp 7 Sep 2011

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications.

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

scikit-learn-contrib/imbalanced-learn 21 Sep 2016

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition.

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

cpapadimitriou/Click-Through-Rate-prediction 4 Nov 2017

In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings.

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

learn-ensemble/R-SUMMA 13 Feb 2018

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods.

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

benedekrozemberczki/karateclub ASONAM 2019 2019

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

General audio tagging with ensembling convolutional neural network and statistical features

Cocoxili/DCASE2018Task2 30 Oct 2018

Audio tagging is challenging due to the limited size of data and noisy labels.

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

softsys4ai/neural-distiller 17 Sep 2019

Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements.