A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks

Corrales, David Camilo; Ledezma, Agapito; Corrales, Juan Carlos

doi:10.1016/j.asoc.2020.106180

Cited by 29 publications

(10 citation statements)

References 30 publications

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“…Khosravani et al [47] offers a case-based reasoning application in a defect detection system for dripper manufacturing. Corrales et al [17] provide a case-based reasoning system for data cleaning algorithm recommendation in classification and regression problems. As the number of stored cases grows, CBR becomes more intelligent and thus might be useful in such scenarios while building a model.…”

Section: Case-based Reasoningmentioning

confidence: 99%

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

2022

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Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on “AI-based Modeling” with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

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Section: Case-based Reasoningmentioning

confidence: 99%

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

2022

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“…However, the technique heavily relied on an accurate selection of hit rate and accuracy to avoid misleading recommendations of an algorithm. Corrales et al [ 40 ] proposed an algorithm recommender for regression and classification problems using case-based reasoning. The limitation of the system lies in that the efficiency declines when there is an increase in search time for similar solved cases.…”

Section: Related Workmentioning

confidence: 99%

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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“…An intracase crossover algorithm is proposed to improve the processing e ect of parallel data and the e ciency of case retrieval in Reference [3]. A cleaning algorithm for regression ltering is put forward in Reference [4], which shortens the time for case retrieval. In Reference [5], the optimization method of the GRNN neural network is used to improve the e ciency of CBR retrieval, realize the self-learning and self-growth of eld problem diagnosis, and e ectively avoid the problems of low matching degree and slow convergence speed of traditional CBR algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis

Hao

Yang

2022

Mathematical Problems in Engineering

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In order to improve the retrieval efficiency, this paper uses case-based reasoning (CBR) in the retrieval of traffic congestion cases and tries to adopt the strategy of clustering case databases before retrieval so as to narrow the scope of case retrieval. In terms of case clustering, the k-means algorithm, with excellent performance in text clustering, is selected to cluster traffic congestion edge cases. At the same time, considering that there is a certain similarity among the descriptions of traffic congestion, the K-means algorithm is optimized to generate an accurate clustering. Those edge cases are clustered into microcase clusters of traffic congestion and then divided into different traffic congestion categories according to the distance of cluster center. Experimental results show that the clustered case base is divided into several microcase bases, which improves the accuracy and shortens the retrieval time in the process of retrieval and provides a new idea for the retrieval method in the process of case-based reasoning.

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A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks

Cited by 29 publications

References 30 publications

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

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

A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis

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