Sample re-weighting hyper box classifier for multi-class data classification

Yang, Lingjian; Liu, Songsong; Tsoka, Sophia; Papageorgiou, Lazaros G.

doi:10.1016/j.cie.2015.02.022

Cited by 16 publications

(14 citation statements)

References 71 publications

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“…Ensemble of classifiers have been used in many different fields (Perikos and Hatzilygeroudis, 2016;Yang et al, 2015). The accuracy obtained by ensembles is usually higher than that of the individual classifiers that comprise them.…”

Section: Ensemblementioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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Size fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical observations in each class.Keywords: Multivariate conditional random forest; Proportional odds logistic regression; Supervised learning; Ordinal classification; Childrenswear garment fitting; Variable importance ACKNOWLEDGEMENTS This work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.An ensemble of ordered logistic regression and random forest for child garment size matchingSize fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical obs...

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Section: Ensemblementioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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“…The original approach they developed used a mixed integer linear programming (MILP) model to generate non-overlapping hyperboxes to enclose clusters of data; additional hyperboxes can be determined iteratively to improve classification performance. Further algorithmic improvements were reported by Maskooki [26] and Yang et al [27]. The presence of user-defined parameters makes the training of hyperbox models highly interactive.…”

Section: Methodsmentioning

confidence: 97%

“…The resulting optimized hyperboxes constitute a rule-based model that can be used as a classification model. One of the key advantages of this technique is that the hyperboxes can be readily interpreted as IF-THEN rules, which can be used effectively and intuitively to support decisions [27].…”

Section: Methodsmentioning

confidence: 99%

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Enhanced Hyperbox Classifier Model for Nanomaterial Discovery

Janairo

Aviso

Promentilla

et al. 2020

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Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis.

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“…ise bir birimi dışarıda tutma çapraz geçerlilik skorlarına göre oluşturulmuştur. IRIS veri seti için (set 1), Yang vd [47]…”

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Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

Doğan¹,

Orman²,

Örkcü³

et al. 2019

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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 A new approach for the multi group classification problems  Using the regression analysis for the obtaining the classification scores  Using the mathematical programming for the classifying of the units Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. In this study, for solving multi-group classification problems, a new two-stage hybrid classification method based on regression analysis and mathematical programming has been developed. In the first step of the proposed method, the classification score of each unit is estimated with the help of the linear regression equation for each unit. In the second step, the classification of the units is performed by the mathematical programming model based on clustering analysis. Figure A. Flow chart of proposed method Purpose: In this study, for solving multi-group classification problems, a new method has been proposed based on regression analysis and mathematical programming classification method. The purpose of this study is handled the multi-group classification problem with the help of the superiority of regression analysis from the statistical theory and the flexibility of mathematical programming by a two-stage detailed examination idea. Theory and Methods: The proposed method combines the strengths of regression analysis and mathematical programming method. Results: From the 10 real data sets taken the well-known literature and simulation study results, it is observed that the proposed method outperforms the regression analysis, mathematical programming and artificial neural network based classification methods. Conclusion:With the proposed method, it is possible to achieve high correct classification success in the multiclass classification problems.

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Sample re-weighting hyper box classifier for multi-class data classification

Cited by 16 publications

References 71 publications

An ensemble of ordered logistic regression and random forest for child garment size matching

An ensemble of ordered logistic regression and random forest for child garment size matching

Enhanced Hyperbox Classifier Model for Nanomaterial Discovery

Çok gruplu sınıflandırma problemlerine regresyon analizi ve matematiksel programlama tabanlı yeni bir yaklaşım

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