Visualization of confusion matrices with network graphs

The use of network analysis as a means of visualizing the off‐diagonal (misclassified) elements of a confusion matrix is demonstrated, and the potential to use the network graphs as a guide for developing hierarchical classification models is presented. A very brief summary of graph theory is described. This is followed by an explanation and code with examples of how these networks can then be used for visualization of confusion matrices. The use of network graphs to provide insight into differing model performance is also addressed.

[1]  M. Rychlik,et al.  Hidden in its color: A molecular-level analysis of the beer's Maillard reaction network. , 2021, Food chemistry.

[2]  A. Clark,et al.  Cluster Identification Using Modularity Optimization to Uncover Chemical Heterogeneity in Complex Solutions. , 2021, The journal of physical chemistry. A.

[3]  Dheer Noal Desai,et al.  The spectral radius of graphs with no odd wheels , 2021, Eur. J. Comb..

[4]  Yuerong Liang,et al.  Roasting process shaping the chemical profile of roasted green tea and the association with aroma features. , 2021, Food chemistry.

[5]  R. Jinnouchi,et al.  Discovering chemical reaction pathways using accelerated molecular dynamics simulations and network analysis tools – Application to oxidation induced decomposition of ethylene carbonate , 2021 .

[6]  R. Schweiggert,et al.  Network analysis on Fourier-transform infrared (FTIR) spectroscopic data sets in an Eigen space layout: Introducing a novel approach for analysing wine samples. , 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[7]  Mahantesh Halappanavar,et al.  A Graph Theoretical Intercomparison of Atmospheric Chemical Mechanisms , 2020, Geophysical Research Letters.

[8]  T. Murakami,et al.  Rescaling the complex network of low-temperature plasma chemistry through graph-theoretical analysis , 2020, Plasma Sources Science and Technology.

[9]  Yiyu Cheng,et al.  A strategy for identifying effective and risk compounds of botanical drugs with LC-QTOF-MS and network analysis: A case study of Ginkgo biloba preparation. , 2020, Journal of pharmaceutical and biomedical analysis.

[10]  Kristin A. Persson,et al.  A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis , 2020, Nature Communications.

[11]  Colin Renfrew,et al.  Phylogenetic network analysis of SARS-CoV-2 genomes , 2020, Proceedings of the National Academy of Sciences.

[12]  Federico Battiston,et al.  Bridging the gap between graphs and networks , 2020, Communications Physics.

[13]  R. Sekhon,et al.  Genome-wide identification, expression profiling, and network analysis of AT-hook gene family in maize. , 2020, Genomics.

[14]  Wenjun Zhang,et al.  Protein-protein interaction network analysis of insecticide resistance molecular mechanism in Drosophila melanogaster. , 2018, Archives of insect biochemistry and physiology.

[15]  X. Qin,et al.  Uncovering the anticancer mechanism of Compound Kushen Injection against HCC by integrating quantitative analysis, network analysis and experimental validation , 2018, Scientific Reports.

[16]  J. Moreno.  Who Shall Survive: A New Approach to the Problem of Human Interrelations , 2017 .

[17]  J. Suykens,et al.  Indefinite kernels in least squares support vector machines and principal component analysis , 2017 .

[18]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[19]  Steven J. M. Jones,et al.  Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. , 2011, The New England journal of medicine.

[20]  I. Schreiber,et al.  Stoichiometric network analysis of the photochemical processes in the mesopause region. , 2011, Physical chemistry chemical physics : PCCP.

[21]  Carter T. Butts,et al.  network: A Package for Managing Relational Data in R , 2008 .

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[24]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[25]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[26]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[27]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[28]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .