Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

Görtler, Jochen; Hohman, Fred; Moritz, Dominik; Wongsuphasawat, Kanit; Ren, Donghao; Nair, Rahul; Kirchner, Marc; Patel, Kayur

doi:10.1145/3491102.3501823

Cited by 41 publications

(33 citation statements)

References 23 publications

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“…The confusion matrix is a summary of the predictions for a classification problem. The number of correct and incorrect predictions is summarized using count values and broken down by each category, which is the key to the confusion matrix ( Görtler et al, 2022 ). The confusion matrix shows which part of the classification model is confused when making predictions, providing insight not only into the errors made by the classification model, but more importantly, the types of errors that occur, overcoming the limitations associated with using classification accuracy alone ( Li et al, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer

et al. 2022

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Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification.

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Section: Resultsmentioning

confidence: 99%

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer

et al. 2022

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“…The confusion matrix is a tabular layout that compares the predicted class labels with the actual class labels in all data instances [23]. Based on Figure 11, the confusion matrix table for the XGBoost scenario using SMOTE can be understood that in the Fraud class there are 91 image data that are predicted correctly and 9 data that are predicted incorrectly.…”

Section: Resultsmentioning

confidence: 99%

Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques

Rahmatullah

Hanani

Naim

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes.

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“…al. [9]. To enhance the most important symbols for large and dense matrices, we suggest to add an interaction like a slider to threshold the display of symbols.…”

Section: Discussionmentioning

confidence: 99%

“…CM have the advantage of being model-agnostic. Instead of filling the cells of a CM using a gradient of colors, NEO [9] encodes the percentages in the cells with rectangles of different sizes.…”

Section: Related Workmentioning

confidence: 99%

Relative Confusion Matrix: Efficient Comparison of Decision Models

Pommé

Bourqui

Giot

2022

2022 26th International Conference Information Visualisation (IV)

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Current machine learning and deep learning approaches are cutting-edge methods for solving classification tasks. Comparing the performances of classification models has become a prominent task since the outbreak of these techniques. The performance of such classification models is measured by the ratio between the correctly predicted samples and the others. The most widely used visualization to represent this information is the Confusion matrix. Yet, if this technique is suited to apprehend one model performances, very few works use this representation to compare models. In that paper, we present the Relative Confusion Matrix (RCM), a new matrix visualization that leverages Confusion matrices and a color encoding to expose the class-wise differences of performances between two models. We conduct a user evaluation to compare RCM with two confusion matrix variants. Our results show that RCM encoding leads to a more efficient comparison of two models than existing approaches.

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Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

Cited by 41 publications

References 23 publications

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer

Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques

Relative Confusion Matrix: Efficient Comparison of Decision Models

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