Search

Machine learning techniques are a driving force for research in various fields, from credit card fraud detection to stock analysis. Recently, a growing interest in increasing human involvement has emerged, with the primary goal of improving the interpretability of machine learning models. Among different techniques, Partial Dependence Plots (PDP) represent one of the main model-agnostic approaches for interpreting how the features influence the prediction of a machine learning model. However, its limitations (i.e., visual interpretation, aggregation of heterogeneous effects, inaccuracy, and computability) could complicate or misdirect the analysis. Moreover, the resulting combinatorial space can be challenging to explore both computationally and cognitively when analyzing the effects of more features at the same time. This article proposes a conceptual framework that enables effective analysis workflows, mitigating state-of-the-art limitations. The proposed framework allows for exploring and refining computed partial dependences, observing incrementally accurate results, and steering the computation of new partial dependences on user-selected subspaces of the combinatorial and intractable space. With this approach, the user can save both computational and cognitive costs, in contrast with the standard monolithic approach that computes all the possible combinations of features on all their domains in batch. The framework is the result of a careful design process involving experts' knowledge during its validation and informed the development of a prototype, W4SP 1 , that demonstrates its applicability traversing its different paths. A case study shows the advantages of the proposed approach.

RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot that leads users to make inaccurate conclusions about points values and data distribution. This article attacks this problem without altering the original RadViz design: It defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This article investigated the intuition that reducing the effectiveness error is beneficial for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this article have been made available in a d3.js plugin at https://aware-diag-sapienza.github.io/d3-radviz.

Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual analytics solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed solution.