An instance-based learning recommendation algorithm of imbalance handling methods

Zhang, Xueying; Li, Ruixian; Zhang, Bo; Yue, Yang; Guo, Jun; Xiang, Ji

doi:10.1016/j.amc.2018.12.020

Cited by 24 publications

(25 citation statements)

References 18 publications

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“…However, the minority class usually is the class of interest and more important. Existing methods such as datalevel [43], algorithmic-level [44],. and cost-sensitive learning [45], [46] have been proposed to address this problem.…”

Section: Weighted Decision-making Tablementioning

confidence: 99%

A Chi-MIC Based Adaptive Multi-Branch Decision Tree

Yang

et al. 2021

IEEE Access

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Since the decision trees (DTs) have an advantage over "black-box" models, such as neural nets or support vector machines, in terms of comprehensibility, such that it might merit improvement for further optimization. The node splitting measures and pruning methods are primary among the techniques that can improve the generalization abilities of DTs. Here, we introduced the unequal interval optimization for node splitting, as well as the local chi-square test for tree pruning. This new method was named an adaptive multibranch decision tree (CMDT). 11 benchmark data sets with different scales were chosen from UCI Machine Learning Repository and coupled with 12 classifiers to evaluate the CMDT algorithm. The results showed that CMDT can be more reliable than the twelve comparative approaches, especially for imbalanced datasets. We also discussed the performance metrics and the weighted decision-making table in unbalanced data sets. The CMDT algorithm can be found here: https://github.com/chenyuan0510/CMDT. INDEX TERMS decision tree, node splitting, Chi-MIC, CMDT, pruning methods.

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Section: Weighted Decision-making Tablementioning

confidence: 99%

A Chi-MIC Based Adaptive Multi-Branch Decision Tree

Yang

et al. 2021

IEEE Access

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“…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%

“…With the use of KNN as meta-learner, the addition of meta examples from new experimental results can quickly be integrated into the existing meta examples in the meta-knowledge database without the requirement of remodeling the relationship between meta-features and performance of candidate algorithms. 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%

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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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“…KNN), the algorithm memorizes the training data samples and generalizes to new observations by matching them to the learned data samples according to a similarity measure. The algorithm stores instances of training data instead of constructing a general internal model [7,12]. In modelbased learning, generalization to new data samples is obtained by predicting them based on a model built on a set of examples [7,13].…”

Section: Instance-based Versus Model-based Learningmentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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An instance-based learning recommendation algorithm of imbalance handling methods

Cited by 24 publications

References 18 publications

A Chi-MIC Based Adaptive Multi-Branch Decision Tree

A Chi-MIC Based Adaptive Multi-Branch Decision Tree

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

Recent evolutions of machine learning applications in clinical laboratory medicine

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