Ensemble Learning Based Classification Algorithm Recommendation

• URL: http://arxiv.org/abs/2101.05993v1
• Date: Fri, 15 Jan 2021 07:14:51 GMT
• Title: Ensemble Learning Based Classification Algorithm Recommendation
• Authors: Guangtao Wang, Qinbao Song and Xiaoyan Zhu
• Abstract summary: This paper proposes an ensemble learning-based algorithm recommendation method. To evaluate the proposed recommendation method, extensive experiments with 13 well-known candidate classification algorithms and five different kinds of meta-features are conducted on 1090 benchmark classification problems.
• Score: 8.94752302607367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining. The existing algorithm recommendation models are generally constructed on only one kind of meta-features by single learners. Considering that i) ensemble learners usually show better performance and ii) different kinds of meta-features characterize the classification problems in different viewpoints independently, and further the models constructed with different sets of meta-features will be complementary with each other and applicable for ensemble. This paper proposes an ensemble learning-based algorithm recommendation method. To evaluate the proposed recommendation method, extensive experiments with 13 well-known candidate classification algorithms and five different kinds of meta-features are conducted on 1090 benchmark classification problems. The results show the effectiveness of the proposed ensemble learning based recommendation method.

Related papers

• Relation-aware Ensemble Learning for Knowledge Graph Embedding [68.94900786314666]
  We propose to learn an ensemble by leveraging existing methods in a relation-aware manner. exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. We propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently.
  arXiv  Detail & Related papers  (2023-10-13T07:40:12Z)
• 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)
• Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering [4.772368796656325]
  This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning. The proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.
  arXiv  Detail & Related papers  (2022-03-18T11:43:12Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• Relearning ensemble selection based on new generated features [0.0]
  The proposed technique was compared with state-of-the-art ensemble methods using three benchmark datasets and one synthetic dataset. Four classification performance measures are used to evaluate the proposed method.
  arXiv  Detail & Related papers  (2021-06-12T12:45:32Z)
• SOAR: Simultaneous Or of And Rules for Classification of Positive & Negative Classes [0.0]
  We present a novel and complete taxonomy of classifications that clearly capture and quantify the inherent ambiguity in noisy binary classifications in the real world. We show that this approach leads to a more granular formulation of the likelihood model and a simulated-annealing based optimization achieves classification performance competitive with comparable techniques.
  arXiv  Detail & Related papers  (2020-08-25T20:00:27Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Learning to Select Base Classes for Few-shot Classification [96.92372639495551]
  We use the Similarity Ratio as an indicator for the generalization performance of a few-shot model. We then formulate the base class selection problem as a submodular optimization problem over Similarity Ratio.
  arXiv  Detail & Related papers  (2020-04-01T09:55:18Z)
• Optimal Clustering from Noisy Binary Feedback [75.17453757892152]
  We study the problem of clustering a set of items from binary user feedback. We devise an algorithm with a minimal cluster recovery error rate. For adaptive selection, we develop an algorithm inspired by the derivation of the information-theoretical error lower bounds.
  arXiv  Detail & Related papers  (2019-10-14T09:18:26Z)

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.