Feature selection in meta learning framework

Shilbayeh, Samar; Vadera, Sunil

doi:10.1109/sai.2014.6918200

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…However, IG and χ 2 rank features based on the data characteristics only, independently from the classifier. Hence, to corroborate these rankings, feature selection was further conducted on the 10 features with relatively high IG as well as high χ 2 values by taking the classifier into account through what is known as a wrapper approach [25]. Specifically, a sequential forward feature selection was adopted to identify the top five features from among the 10 with relatively high IG and χ 2 values [21].…”

Section: Resultsmentioning

confidence: 99%

Distinguishing between authentic and fictitious user-generated hotel reviews

Banerjee

Chua

Kim

2015

2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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Section: Resultsmentioning

confidence: 99%

Distinguishing between authentic and fictitious user-generated hotel reviews

Banerjee

Chua

Kim

2015

2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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“…This project also introduced the concept of landmarking, which adopts the idea of using a simple learning algorithm to understand the place of a specific problem space. The idea of landmarking is extended to relative landmarking (the relative order between landmarking algorithms) and sub-sampling, which is the use of a landmark on a sample of the data is used in Shilbayeh and Vadera (2014), Alcobaça et al (2020) and Shen et al (2020).…”

Section: Meta-learning Frameworkmentioning

confidence: 99%

Cost-sensitive meta-learning framework

Shilbayeh

Vadera

2021

JM2

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Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

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“…As more recent studies, we can cite [42], in which a study was conducted for the construction of a symbolic recommendation model of the best Feature Selection algorithm. In addition, a lazy method was presented in [43] for the recommendation of Feature Selection algorithms, while a metalearning framework was developed in [44] to learn which Feature Selection algorithms are more suitable for a given dataset. The authors in [45] used five different categories of state-of-the-art meta-features to characterize datasets, and built a different regression model to connect datasets to each candidate algorithm.…”

Section: Related Workmentioning

confidence: 99%

Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning

et al. 2021

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Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent decision making solutions to complex real problems. Among the different ML structures, a classifier ensemble has been successfully applied to several classification domains. A classifier ensemble is composed of a set of classifiers (specialists) organized in a parallel way, and it is able to produce a combined decision for an input pattern (instance). Although Classifier ensembles have proved to be robust in several applications, an important issue is always brought to attention is the ensemble's structure. In other words, the correction definition of its structure, like the number and type of classifiers and the aggregation method, has an important role in its performance. Usually, an exhaustive testing and evaluation process is required to better define the ideal structure for an ensemble. Aiming to produce an interesting investigation in this field, this paper proposes two new approaches for automatic recommendation of classifier ensemble structure, using meta-learning to recommend three of these important parameters: type of classifier, number of base classifiers, and the aggregation method. The main aim is to provide a robust structure in a simple and fast way. In this analysis, five well known classification algorithms will be used as base classifiers of the ensemble: kNN (Nearest Neighbors), DT (Decision Tree), RF (Random Forest), NB (Naive Bayes) e LR (Logistic Regression). Additionally, the classifier ensembles will be evaluated using seven different strategies as aggregation functions: HV (Hard Voting), SV (Soft Voting), LR (Logistic Regression), SVM (Support Vector Machine), NB(Naive Bayes), MLP (Multilayer perceptron) e DT (Decision Tree). The empirical analysis shows that our approach can lead to robust classifier ensembles, for the majority of the analysed cases.INDEX TERMS Classifier ensembles, Meta-learning, Multiple classifier system, Machine Learning.

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Feature selection in meta learning framework

Cited by 12 publications

References 14 publications

Distinguishing between authentic and fictitious user-generated hotel reviews

Distinguishing between authentic and fictitious user-generated hotel reviews

Cost-sensitive meta-learning framework

Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning

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