A new classification algorithm recommendation method based on link prediction

Zhu, Xiaoyan; Yang, Xiaomei; Ying, Chenzhen; Wang, Guangtao

doi:10.1016/j.knosys.2018.07.015

Cited by 23 publications

(21 citation statements)

References 29 publications

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“…The objective of mapping model construction is to model the relationship between dataset characteristics (meta-features) and performance information of candidate algorithms. In latest approaches [6], [12], [13], [46], the meta-knowledge is first transformed into a learning dataset so that a model can be train on them. Transformation of the meta-data into a learning dataset involves determining the set of best performing algorithms for each dataset in the knowledge-base.…”

Section: Meta-learning Framework For Classifier Selectionmentioning

confidence: 99%

“…Transformation of the meta-data into a learning dataset involves determining the set of best performing algorithms for each dataset in the knowledge-base. According to the recommendation of Demsar [47] and frequently followed procedure in previous similar studies in literature [6], [12], [13], [46], the multiple comparison procedure (MCP) is recommended for identifying the best performing algorithms for each dataset. The non parametric MCP, Friedman test followed by Holm procedure test are performed at significance level of 0.05 in order to identify best performing algorithms among the candidate algorithm for each dataset such that the difference in performance of identified algorithms are not statistically significant.…”

Section: Meta-learning Framework For Classifier Selectionmentioning

confidence: 99%

“…These measures were improved and extended to ordinary classification problems in later studies [6]. This group of meta-features has shown its significance in algorithm recommendation systems [12], [67]. It adopt frequencies of itemsets with respect to the parity function to characterize a dataset [66].…”

Section: ) Structural-information-based Measuresmentioning

confidence: 99%

“…This approach is used in majority of the studies for classifier selection, examples includes [39], [46], [51], [66], [67], [71]- [73]. However, the drawback of this approach is the selection of optimal value for the parameter K because the number of similar datasets vary for each problem in the meta-knowledge database [12].…”

Section: B Meta-learnermentioning

confidence: 99%

“…Metalearning imitate this strategy by accumulating the knowledge obtained from the application of algorithms on similar tasks and then use that knowledge to recommend a set of potentially appropriate algorithms for a specific task [11]. The typical area of meta-learning for algorithm recommendation is classification [12], [13], however it has also demonstrated success for algorithm selection in a number of other domains that includes clustering [14]- [16], instance selection [17], [18] regression [19] optimization [20]- [24] time series [25]- [27] and dynamic ensemble selection for performance improvement of classification problems [28]- [32].…”

Section: Introductionmentioning

confidence: 99%

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A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection

et al. 2020

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Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.

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Section: Meta-learning Framework For Classifier Selectionmentioning

confidence: 99%

Section: Meta-learning Framework For Classifier Selectionmentioning

confidence: 99%

Section: ) Structural-information-based Measuresmentioning

confidence: 99%

Section: B Meta-learnermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection

et al. 2020

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Model Selection Using Graph Neural Networks

Nápoles,

Grau,

Güven

et al. 2024

Lecture Notes in Networks and Systems

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EHHR: an efficient evolutionary hyper-heuristic based recommender framework for short-text classifier selection

2022

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With various machine learning heuristics, it becomes difficult to choose an appropriate heuristic to classify short-text emerging from various social media sources in the form of tweets and reviews. The No Free Lunch theorem asserts that no heuristic applies to all problems indiscriminately. Regardless of their success, the available classifier recommendation algorithms only deal with numeric data. To cater to these limitations, an umbrella classifier recommender must determine the best heuristic for short-text data. This paper presents an efficient reminisce-enabled classifier recommender framework to recommend a heuristic for new short-text data classification. The proposed framework, “Efficient Evolutionary Hyper-heuristic based Recommender Framework for Short-text Classifier Selection (EHHR),” reuses the previous solutions to predict the performance of various heuristics for an unseen problem. The Hybrid Adaptive Genetic Algorithm (HAGA) in EHHR facilitates dataset-level feature optimization and performance prediction. HAGA reveals that the influential features for recommending the best short-text heuristic are the average entropy, mean length of the word string, adjective variation, verb variation II, and average hard examples. The experimental results show that HAGA is 80% more accurate when compared to the standard Genetic Algorithm (GA). Additionally, EHHR clusters datasets and rank heuristics cluster-wise. EHHR clusters 9 out of 10 problems correctly.

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A new classification algorithm recommendation method based on link prediction

Cited by 23 publications

References 29 publications

A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection

A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection

Model Selection Using Graph Neural Networks

EHHR: an efficient evolutionary hyper-heuristic based recommender framework for short-text classifier selection

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