A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning

• URL: http://arxiv.org/abs/2101.10267v1
• Date: Tue, 15 Dec 2020 09:33:12 GMT
• Title: A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning
• Authors: Mansoureh Maadia, Uwe Aickelin, Hadi Akbarzadeh Khorshidi
• Abstract summary: This paper focuses on the classifiers outputs aggregation step and presents a new interval-based aggregation modeling using bagging resampling approach and Interval Agreement Approach (IAA) in ensemble learning. In this paper, in addition to implementing a new aggregation approach in ensemble learning, we designed some experiments to encourage researchers to use interval modeling in ensemble learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The main aim in ensemble learning is using multiple individual classifiers outputs rather than one classifier output to aggregate them for more accurate classification. Generating an ensemble classifier generally is composed of three steps: selecting the base classifier, applying a sampling strategy to generate different individual classifiers and aggregation the classifiers outputs. This paper focuses on the classifiers outputs aggregation step and presents a new interval-based aggregation modeling using bagging resampling approach and Interval Agreement Approach (IAA) in ensemble learning. IAA is an interesting and practical aggregation approach in decision making which was introduced to combine decision makers opinions when they present their opinions by intervals. In this paper, in addition to implementing a new aggregation approach in ensemble learning, we designed some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and this leads to more accurate classification. For this purpose, we compared the results of implementing the proposed method to the majority vote as the most common and successful aggregation function in the literature on 10 medical data sets to show the better performance of the interval modeling and the proposed interval-based aggregation function in binary classification when it comes to ensemble learning. The results confirm the good performance of our proposed approach.

Related papers

• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Generating Hierarchical Structures for Improved Time Series Classification Using Stochastic Splitting Functions [0.0]
  This study introduces a novel hierarchical divisive clustering approach with splitting functions (SSFs) to enhance classification performance in multi-class datasets through hierarchical classification (HC) The method has the unique capability of generating hierarchy without requiring explicit information, making it suitable for datasets lacking prior knowledge of hierarchy.
  arXiv  Detail & Related papers  (2023-09-21T10:34:50Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• An Evolutionary Approach for Creating of Diverse Classifier Ensembles [11.540822622379176]
  We propose a framework for classifier selection and fusion based on a four-step protocol called CIF-E. We implement and evaluate 24 varied ensemble approaches following the proposed CIF-E protocol. Experiments show that the proposed evolutionary approach can outperform the state-of-the-art literature approaches in many well-known UCI datasets.
  arXiv  Detail & Related papers  (2022-08-23T14:23:27Z)
• Federated Learning Aggregation: New Robust Algorithms with Guarantees [63.96013144017572]
  Federated learning has been recently proposed for distributed model training at the edge. This paper presents a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. We derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses.
  arXiv  Detail & Related papers  (2022-05-22T16:37:53Z)
• Evolving Multi-Label Fuzzy Classifier [5.53329677986653]
  Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner.
  arXiv  Detail & Related papers  (2022-03-29T08:01:03Z)
• Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing [72.4445825335561]
  We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers. Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores. While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems.
  arXiv  Detail & Related papers  (2021-06-11T13:03:33Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories [1.3706331473063877]
  The majority vote is an example of a methodology used to combine classifiers in an ensemble method. We introduce ensemble methods based on recurrent correlation associative memories for binary classification problems.
  arXiv  Detail & Related papers  (2020-09-18T01:16:53Z)
• Covariance-engaged Classification of Sets via Linear Programming [16.11804985840274]
  Set classification aims to classify a set of observations as a whole, as opposed to classifying individual observations separately. We show that the number of observations in the set plays a critical role in bounding the Bayes risk. Under this framework, we propose new methods of set classification.
  arXiv  Detail & Related papers  (2020-06-26T07:20:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.