A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

Espezua, Soledad; Villanueva, Edwin; Maciel, Carlos Dias; Carvalho, André Carlos Ponce de Leon Ferreira

doi:10.1016/j.neucom.2014.07.057

Cited by 34 publications

(15 citation statements)

References 76 publications

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“…Meanwhile, the PDA index is based in the penalty of the LDA index being applied in situations with many predictors highly correlated when the classification is necessary. However, the LDA index is obtained through the linear discriminant analysis with the objective of searching for linear projections with the highest separation among classes and the lowest intraclass dispersion (Espezua, et al, 2015).…”

Section: Projection Pursuitmentioning

confidence: 99%

Classification of specialty coffees using machine learning techniques

Ossani

Rossoni

Cirillo

et al. 2021

RSD

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Specialty coffees have a big importance in the economic scenario, and its sensory quality is appreciated by the productive sector and by the market. Researches have been constantly carried out in the search for better blends in order to add value and differentiate prices according to the product quality. To accomplish that, new methodologies must be explored, taking into consideration factors that might differentiate the particularities of each consumer and/or product. Thus, this article suggests the use of the machine learning technique in the construction of supervised classification and identification models. In a sensory evaluation test for consumer acceptance using four classes of specialty coffees, applied to four groups of trained and untrained consumers, features such as flavor, body, sweetness and general grade were evaluated. The use of machine learning is viable because it allows the classification and identification of specialty coffees produced in different altitudes and different processing methods.

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Section: Projection Pursuitmentioning

confidence: 99%

Classification of specialty coffees using machine learning techniques

Ossani

Rossoni

Cirillo

et al. 2021

RSD

View full text Add to dashboard Cite

show abstract

“…The LDA index, mentioned by Espezua, Villanueva, Maciel, and Carvalho (2015) and Lee, Cook, Klinke, and Lumley (2005) is based on the linear discriminant analysis and it is applied in the exploratory supervised classification. This index is set for one or more dimensions, and it uses the ideas of Fisher's linear discriminant in the search of projections for classification, favoring the linear projection with the highest class separation and the lowest intraclass dispersion, defined by the Eq.…”

Section: Projection Pursuitmentioning

confidence: 99%

Machine learning in classification and identification of nonconventional vegetables

Ossani

Souza

Rossoni

et al. 2020

Journal of Food Science

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Vegetables are important in economic, social, and nutritional matters in both the Brazilian and international scenes. Hence, some researches have been carried out in order to encourage the production and consumption of different species such as nonconventional vegetables. These vegetables have an added value because of their nutritional quality and nostalgic appeal due to the reintroduction of these species. For this reason, this article proposes the use of the machine learning technique in the construction of models for supervised classification and identification in an experiment with five leafy special of nonconventional vegetables (Tropaeolum majus, Rumex acetosa, Stachys byzantina, Lactuca cf. indica e Pereskia aculeata) assessing the characteristics of the macro and micro nutrients. In order to evaluate the classifiers' performance, the cross-validation procedure via Monte Carlo simulation was considered to confirm the model. In ten replications, the success and error rates were obtained, considering the false positive and false negative rates, sensibility, and accuracy of the classification method. Thus, it was concluded that the use of machine learning is viable because it allows the classification and identification of nonconventional vegetables using few nutritional attributes and obtaining a success rate of over 89% in most of the classifiers tested.

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“…For the feature selection problem, the classification performance and stability are discussed. Espezua et al [27] compressed data rapidly and then used an improved projection tracking method to avoid dimension disasters. Hira and Gillies [28] summarized various methods about dimension reduction for high-dimensional microarray data.…”

Section: Introductionmentioning

confidence: 99%

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

Xiao

Shi

et al. 2020

Computational Intelligence and Neuroscience

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The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

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A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

Cited by 34 publications

References 76 publications

Classification of specialty coffees using machine learning techniques

Classification of specialty coffees using machine learning techniques

Machine learning in classification and identification of nonconventional vegetables

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

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