Classification of Multivariate Data Sets without Missing Values Using Memory Based Classifiers - An Effectiveness Evaluation

Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods.

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Classification of jujube defects in small data sets based on transfer learning

Neural Computing and Applications ◽

10.1007/s00521-021-05715-2 ◽

2021 ◽

Author(s):

Jianping Ju ◽

Hong Zheng ◽

Xiaohang Xu ◽

Zhongyuan Guo ◽

Zhaohui Zheng ◽

...

Keyword(s):

Transfer Learning ◽

Loss Function ◽

Training Model ◽

Parameter Distribution ◽

Test Accuracy ◽

Small Data ◽

Data Sets ◽

Data Set ◽

Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

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Classification of Edible Vegetable Oil Degradation Using Multivariate Data Analysis From Electrochemical Techniques

Food Analytical Methods ◽

10.1007/s12161-021-02083-4 ◽

2021 ◽

Author(s):

Sandra Ramirez-Montes ◽

Eva M. Santos ◽

Carlos A. Galan-Vidal ◽

J. Andres Tavizon-Pozos ◽

Jose A. Rodriguez

Keyword(s):

Data Analysis ◽

Vegetable Oil ◽

Multivariate Data Analysis ◽

Electrochemical Techniques ◽

Multivariate Data ◽

Oil Degradation ◽

Edible Vegetable Oil

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Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2021.3076265 ◽

2021 ◽

pp. 1-1

Author(s):

Adam Kiersztyn ◽

Pawe Karczmarek ◽

Krystyna Kiersztyn ◽

Witold Pedrycz

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Information Granules

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Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Microbiology Research ◽

10.3390/microbiolres12020022 ◽

2021 ◽

Vol 12 (2) ◽

pp. 317-334

Author(s):

Omar Alaqeeli ◽

Li Xing ◽

Xuekui Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Classification Tree ◽

Area Under The Curve ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Tree Algorithms ◽

R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

Information Visualization ◽

10.1177/1473871619858937 ◽

2019 ◽

Vol 19 (1) ◽

pp. 3-23

Author(s):

Aurea Soriano-Vargas ◽

Bernd Hamann ◽

Maria Cristina F de Oliveira

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Multivariate Data ◽

Visual Exploration ◽

Data Sets ◽

Time Varying ◽

Domain Experts ◽

Data Mining Algorithms ◽

Temporal Relationships ◽

Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

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Identifying VIV Vibration Modes by Use of the Empirical Orthogonal Functions Technique

21st International Conference on Offshore Mechanics and Arctic Engineering, Volume 1 ◽

10.1115/omae2002-28425 ◽

2002 ◽

Cited By ~ 3

Author(s):

Gudmund Kleiven

Keyword(s):

Model Test ◽

Vibration Mode ◽

Multivariate Data ◽

Empirical Orthogonal Functions ◽

Mode Shapes ◽

Data Sets ◽

Vibration Modes ◽

Ocean Engineering ◽

Orthogonal Functions ◽

Related Technique

The Empirical Orthogonal Functions (EOF) technique has widely being used by oceanographers and meteorologists, while the Singular Value Decomposition (SVD being a related technique is frequently used in the statistics community. Another related technique called Principal Component Analysis (PCA) is observed being used for instance in pattern recognition. The predominant applications of these techniques are data compression of multivariate data sets which also facilitates subsequent statistical analysis of such data sets. Within Ocean Engineering the EOF technique is not yet widely in use, although there are several areas where multivariate data sets occur and where the EOF technique could represent a supplementary analysis technique. Examples are oceanographic data, in particular current data. Furthermore data sets of model- or full-scale data of loads and responses of slender bodies, such as pipelines and risers are relevant examples. One attractive property of the EOF technique is that it does not require any a priori information on the physical system by which the data is generated. In the present paper a description of the EOF technique is given. Thereafter an example on use of the EOF technique is presented. The example is analysis of response data from a model test of a pipeline in a long free span exposed to current. The model test program was carried out in order to identify the occurrence of multi-mode vibrations and vibration mode amplitudes. In the present example the EOF technique demonstrates the capability of identifying predominant vibration modes of inline as well as cross-flow vibrations. Vibration mode shapes together with mode amplitudes and frequencies are also estimated. Although the present example is not sufficient for concluding on the applicability of the EOF technique on a general basis, the results of the present example demonstrate some of the potential of the technique.

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An Information-Aware Framework for Exploring Multivariate Data Sets

IEEE Transactions on Visualization and Computer Graphics ◽

10.1109/tvcg.2013.133 ◽

2013 ◽

Vol 19 (12) ◽

pp. 2683-2692 ◽

Cited By ~ 36

Author(s):

Ayan Biswas ◽

Soumya Dutta ◽

Han-Wei Shen ◽

Jonathan Woodring

Keyword(s):

Multivariate Data ◽

Data Sets

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A proposed procedure for the analysis of large phytosociological data sets in the classification of South African grasslands

Koedoe ◽

10.4102/koedoe.v38i1.303 ◽

1995 ◽

Vol 38 (1) ◽

Cited By ~ 6

Author(s):

G.J. Bredenkamp ◽

H. Bezuidenhout

Keyword(s):

South African ◽

Plant Communities ◽

Vegetation Type ◽

Second Step ◽

Data Sets ◽

Vegetation Types ◽

Related Plant ◽

Single Cluster ◽

Synoptic Table

A procedure for the effective classification of large phytosociological data sets, and the combination of many data sets from various parts of the South African grasslands is demonstrated. The procedure suggests a region by region or project by project treatment of the data. The analyses are performed step by step to effectively bring together all releves of similar or related plant communities. The first step involves a separate numerical classification of each subset (region), and subsequent refinement by Braun- Blanquet procedures. The resulting plant communities are summarised in a single synoptic table, by calculating a synoptic value for each species in each community. In the second step all communities in the synoptic table are classified by numerical analysis, to bring related communities from different regions or studies together in a single cluster. After refinement of these clusters by Braun-Blanquet procedures, broad vegetation types are identified. As a third step phytosociological tables are compiled for each iden- tified broad vegetation type, and a comprehensive abstract hierarchy constructed.

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