Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.
On the one hand, visualization has been increasingly adopted by the machine learning community to analyze @cite_31 , debug @cite_14 , and present @cite_39 machine learning models. On the other hand, a number of human-in-the-loop methods have been proposed as competitive replacements of full-automatic machine learning methods. These methods include: visual classification @cite_19 @cite_23 @cite_37 , visual optimization @cite_10 @cite_27 , and visual feature engineering @cite_12 @cite_3 @cite_32 .
@cite_37 @cite_14 @cite_32 @cite_3 @cite_39 @cite_19 @cite_27 @cite_23 @cite_31 @cite_10 @cite_12
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Machine learning requires an effective combination of data, features, and algorithms. While many tools exist for working with machine learning data and algorithms, support for thinking of new features, or feature ideation, remains poor. In this paper, we investigate two general approaches to support feature ideation: visual summaries and sets of errors. We present FeatureInsight, an interactive visual analytics tool for building new dictionary features (semantically related groups of words) for text classification problems. FeatureInsight supports an error-driven feature ideation process and provides interactive visual summaries of sets of misclassified documents. We conducted a controlled experiment evaluating both visual summaries and sets of errors in FeatureInsight. Our results show that visual summaries significantly improve feature ideation, especially in combination with sets of errors. Users preferred visual summaries over viewing raw data, and only preferred examining sets when visual summaries were provided. We discuss extensions of both approaches to data types other than text, and point to areas for future research. Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset. An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification. Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclass classification problems. Squares supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data. Our controlled study shows that practitioners can assess performance significantly faster and more accurately with Squares than a confusion matrix, a common performance analysis tool in machine learning. One main task for domain experts in analysing their nD data is to detect and interpret class cluster separations and outliers. In fact, an important question is, which features dimensions separate classes best or allow a cluster-based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class cluster separation? The projections involve the complete set of dimensions features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates DSC, which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster-aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster class separation analysis with a couple of experiments on real-life benchmark high-dimensional data sets.