Pub Date : 2022-05-09DOI: 10.1109/IV56949.2022.00065
Alex Worland, S. Wagle, B. Kovalerchuk
Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three standard benchmarks.
{"title":"Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models","authors":"Alex Worland, S. Wagle, B. Kovalerchuk","doi":"10.1109/IV56949.2022.00065","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00065","url":null,"abstract":"Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three standard benchmarks.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-25DOI: 10.1109/IV56949.2022.00074
R. Francese, M. Frasca, M. Risi, G. Tortora
Acute Leukemia is classified in terms of two distinct classes: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). This paper aims at defining a feature selection analysis process mainly based on Deep Learning for classifying the acute leukemia type. The considered dataset consists in data of patients affected by both the leukemia types. Both the leukemia types are characterized by a list of identical genes for all the patients. The analysis exploits feature selection techniques for reducing the consistent number of variables (genes). To this aim, we use linear models for differential expression for microarray data, and an autoencoder based unsupervised deep learning model to simplify and speed up the classification. Then, classification models have been implemented with the use of a deep neural network (DNN), obtaining an accuracy of approximately 92%. Moreover, the results have been compared with the ones provided by an approach based on support vector machines (SVM), giving an accuracy of 87,39%. Another feature selection approach based on genetic algorithms has been experimented, with worse performances. We also conducted a gene enrichment analysis based on the functional annotation of the differentially expressed genes. As a result, a differentially expressed pathway between the two pathologies has been detected.
{"title":"A deep learning and genetic algorithm based feature selection processes on Leukemia Data","authors":"R. Francese, M. Frasca, M. Risi, G. Tortora","doi":"10.1109/IV56949.2022.00074","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00074","url":null,"abstract":"Acute Leukemia is classified in terms of two distinct classes: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). This paper aims at defining a feature selection analysis process mainly based on Deep Learning for classifying the acute leukemia type. The considered dataset consists in data of patients affected by both the leukemia types. Both the leukemia types are characterized by a list of identical genes for all the patients. The analysis exploits feature selection techniques for reducing the consistent number of variables (genes). To this aim, we use linear models for differential expression for microarray data, and an autoencoder based unsupervised deep learning model to simplify and speed up the classification. Then, classification models have been implemented with the use of a deep neural network (DNN), obtaining an accuracy of approximately 92%. Moreover, the results have been compared with the ones provided by an approach based on support vector machines (SVM), giving an accuracy of 87,39%. Another feature selection approach based on genetic algorithms has been experimented, with worse performances. We also conducted a gene enrichment analysis based on the functional annotation of the differentially expressed genes. As a result, a differentially expressed pathway between the two pathologies has been detected.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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