• Corpus ID: 235795173

Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning

  title={Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning},
  author={Sridevi Narayana Wagle and Boris Kovalerchuk},
Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with… 



Visual Knowledge Discovery and Machine Learning

This book vastly expands the class of reversible lossless 2-D and 3-D visualization methods which preserve the n-D information for the knowledge discovery, called the General Lines Coordinates (GLCs), which is accompanied by a set of algorithms for n- D data classification, clustering, dimension reduction, and Pareto optimization.

Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning

Data visualization combined with self-service or democratized machine learning is proposed in the form of the Iterative Logical Classifier (ILC) algorithm with an added advantage of outperforming the accuracies of black-box machine learning classifiers on benchmark datasets.

Interpretable Machine Learning with Boosting by Boolean Algorithm

  • Nathan NeuhausBoris Kovalerchuk
  • Computer Science
    2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)
  • 2019
A new interpretable algorithm RPPR is proposed, to bridge the gap between the prediction accuracy of interpretable and non-interpretable algorithms on these data, by boosting DCP via discovering properties of misclassified cases.

Knowledge discovery from database Using an integration of clustering and classification

The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.

Enhancement of Cross Validation Using Hybrid Visual and Analytical Means with Shannon Function

This paper is improving the cross validation approach using the combined visual and analytical means in a hybrid setting and involves the adaptation of the Shannon function to obtain the worst case error estimate.

Auto-sklearn: Efficient and Robust Automated Machine Learning

A robust new AutoML system based on the Python machine learning package scikit-learn, which improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization.

Automated Machine Learning: Methods, Systems, Challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and

Human Interface and the Management of Information. Interaction, Visualization, and Analytics

  • H. Mori
  • Computer Science
    Lecture Notes in Computer Science
  • 2018
This work wants to show that dynamic diagrams, whose content is modified and adapted in realtime by monitoring developer’s actions can be of great benefit as their contents are perfectly suited to the developer current task.

An Empirical Comparison of Data Mining Classification Methods

A comparative study of the performance of C4.5, Naive Bayes, SVM and KNN Classification Algorithms is performed.

Optimizing a radial visualization with a genetic algorithm

This paper extends POIViz to Gen-POIViz by proposing a genetic algorithm (GA) that can greatly optimize the quality of the visualization and obtains results with a quality that is between force-directed Multidimensional Scaling (MDS) and Principal Components Analysis (PCA).