methodology-8

Ensemble Learning

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in ensemble-learning-16

Trend

Dataset

Best Model

Actions

SMS Spam Collection Data Set

Libraries

i

Use these libraries to find ensemble-learning-16 models and implementations

KaiyangZhou/Dassl.pytorch

2 papers 1,111

Datasets

SMS Spam Collection Data Set

Wild-Time

Replication Data for: Online Learning with Optimism and Delay

Subtasks

No subtasks available.

Most implemented papers

Masksembles for Uncertainty Estimation

P. Fua, P. Baqué, Timur M. Bagautdinov, N. Durasov•Mon Dec 14 2020

Instead of randomly dropping parts of the network as in MC-dropout, Masksemble relies on a fixed number of binary masks, which are parameterized in a way that allows to change correlations between individual models.

102

Content

0

Paper Graph

Gossip learning with linear models on fully distributed data

Róbert Ormándi, István Hegedüs, Márk Jelasity•Tue Sep 06 2011

This work proposes gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods.

181 0

Paper Graph

Predicting the direction of stock market prices using random forest

Luckyson Khaidem, Snehanshu Saha, Sudeepa Roy Dey•Thu Apr 28 2016

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, which is an ensemble of multiple decision trees.

262 0

Paper Graph

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

Jianfeng Gao, Weizhu Chen, Xiaodong Liu, Pengcheng He•Fri Apr 19 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 and shows that the distilled MT-dNN significantly outperforms the original MT- DNN on 7 out of 9 GLUE tasks.

189 0

Paper Graph

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

Kamelia Aryafar, Devin Guillory, Liangjie Hong•Sun Aug 13 2017

This paper provides a holistic view of Etsy's promoted listings' CTR prediction system and proposes an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings, and compares the system to non-trivial baselines on a large-scale real world dataset from Etsy.

21 0

Paper Graph

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

M. Ahsen, G. Stolovitzky, Robert M. Vogel•Mon Feb 12 2018

A new theoretical framework for binary classification, the Strategy for Unsupervised Multiple Method Aggregation (SUMMA), is developed to estimate the performances of base classifiers and an optimal strategy for ensemble learning from unlabeled data.

5 0

Paper Graph

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

Melih Barsbey, Riza Ozccelik, Arzucan Ozgur, Alperen Baug, Berk Atil, Elif Ozkirimli•Sat Jul 03 2021

The results suggest that models trained in the DebiasedDTA framework can achieve improved generalizability in predicting the interactions of the previously unseen biomolecules, as well as performance improvements on those previously seen.

1 0

Paper Graph

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

G. Lemaître, Fernando Nogueira, Christos K. Aridas•Tue Sep 20 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.

2370 0

Paper Graph

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

Huan Liu, Jundong Li, Liang Wu•Sat Aug 25 2018

A novel multi-level network embedding framework BoostNE, which can learn multiple node embeddings of different granularity from coarse to fine without imposing the prevalent global low-rank assumption, is proposed.

35 0

Paper Graph

General audio tagging with ensembling convolutional neural network and statistical features

Qiuqiang Kong, Boqing Zhu, Haibo Mi, Bo Ding, Dezhi Wang, Huaimin Wang•Mon Oct 29 2018

An ensemble learning framework is applied to ensemble statistical features and the outputs from the deep classifiers, with the goal to utilize complementary information to address the noisy label problem within the framework.

34 0

Paper Graph

Adding a benchmark result helps the community track progress.