Algorithm Selection for Classification Problems via Cluster-based Meta-features

Ler, Daren; Teng, H.S.; He, Yu; Gidijala, Rahul

doi:10.1109/bigdata.2018.8621982

Cited by 4 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, other works focused on analyzing and creating frameworks for meta-features, without diving deep into the algorithm selection problem [59][60][61][62][63].…”

Section: Clarifications On the Excluded Recordsmentioning

confidence: 99%

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Jos�Garc韆-Pe馻lvo¹,

V醶quez-Ingelmo²,

Garc韆-Holgado³

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models' outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

show abstract

“…On the other hand, other works focused on analyzing and creating frameworks for meta-features, without diving deep into the algorithm selection problem [59][60][61][62][63].…”

Section: Clarifications On the Excluded Recordsmentioning

confidence: 99%

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study

Jos�Garc韆-Pe馻lvo¹,

V醶quez-Ingelmo²,

Garc韆-Holgado³

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Thus, they use only the predictive attributes and are indirectly extracted, demanding a set of hyperparameter values, such as the clustering algorithm, its hyperparameters and, possibly, a distance function. By ignoring the classes to create partitions of instances, this group of measures can capture an intrinsic complexity present in the data [87].…”

Section: Clusteringmentioning

confidence: 99%

“…Table 8 presents a list of clustering measures found in MtL studies [44][45][46]87]. Here, k denotes the number of clusters, such that k ≪ n and k ≈ q is a reasonable value to consider.…”

Section: Clusteringmentioning

confidence: 99%

“…The distribution of the clusters based on the instances' frequency is captured by the measure sizeDist. A distribution skewed to the right indicates a complex dataset [87]. If the classes are used to define the clusters, sizeDist becomes similar to the freqClass.…”

Section: Clusteringmentioning

confidence: 99%

“…represents the probability of the uniform partition distribution.purityRatio Ratio of the number of clusters with a given class[87]:[ s 1 k , . .…”

mentioning

confidence: 99%

See 2 more Smart Citations