325 research outputs found
Multi-criteria Performance Assessment of Adaptive Radar Resources Management: Application to Naval Scenario
International audienceMultifunction radars (MFR) must achieve their capability requirements in an increasingly complex environment, populated with diverse and hostile targets (e.g. low Radar Cross-Section, low speed targets in clutter or high speed, ballistic targets) in saturating scenarios (due to e.g. RF interference or threats). These radar systems are increasingly exploiting active electronically scanned array (AESA) technology to dynamically schedule the use of multiple functions in a short duration. However, the increasing complexity and adaptive nature of MFR radar makes it very difficult to specify their performance in a way which can both deliver the required capability and which can be verified in a cost-effective manner. Due to their multi-function feature, the operational scenarios have a strong impact on MFR radars; their performances should be specified accordingly. Addressing the challenges in this area will benefit via better understood requirements which can be more easily interpreted. After definition of FoM (Figures of Merit) for phased array radar operation and performance, we have computed them on benchmark test scenarios of varying complexity. We describe a new methodology to aggregate these metrics to provide a global notation of MFR radar performances
The Shapley Value of Classifiers in Ensemble Games
What is the value of an individual model in an ensemble of binary
classifiers? We answer this question by introducing a class of transferable
utility cooperative games called \textit{ensemble games}. In machine learning
ensembles, pre-trained models cooperate to make classification decisions. To
quantify the importance of models in these ensemble games, we define
\textit{Troupe} -- an efficient algorithm which allocates payoffs based on
approximate Shapley values of the classifiers. We argue that the Shapley value
of models in these games is an effective decision metric for choosing a high
performing subset of models from the ensemble. Our analytical findings prove
that our Shapley value estimation scheme is precise and scalable; its
performance increases with size of the dataset and ensemble. Empirical results
on real world graph classification tasks demonstrate that our algorithm
produces high quality estimates of the Shapley value. We find that Shapley
values can be utilized for ensemble pruning, and that adversarial models
receive a low valuation. Complex classifiers are frequently found to be
responsible for both correct and incorrect classification decisions.Comment: Source code is available here:
https://github.com/benedekrozemberczki/shaple
Explainable Artificial Intelligence for Digital Forensics: Opportunities, Challenges and a Drug Testing Case Study
Forensic analysis is typically a complex and time-consuming process requiring forensic investigators to collect and analyse different pieces of evidence to arrive at a solid recommendation. Our interest lies in forensic drug testing, where evidence comprises a multitude of experimentally obtained data from samples (e.g. hair or nails), occasionally combined with questionnaire data, with a goal of quantifying the likelihood of drug use. The availability of intelligent data-driven technologies can support holistic decision-making in such scenarios, but this needs to be done in a transparent fashion (as opposed to using black-box models). To this end, this book chapter investigates the opportunities and challenges of developing interactive and eXplainable Artificial Intelligence (XAI) systems to support digital forensics and automate the decision-making process to enable fast and reliable generation of evidence for the court of law. Relevant XAI techniques and their applications in forensic testing, including feature section, missing data handling, XAI for multi-criteria and interactive learning, are discussed in detail. A case study on a forensic science company is used to demonstrate the real challenges of forensic reporting and potential for making use of forensic data to pave the way for future research towards XAI-driven digital forensics
From Anecdotal Evidence to Quantitative Evaluation Methods:A Systematic Review on Evaluating Explainable AI
The rising popularity of explainable artificial intelligence (XAI) to
understand high-performing black boxes, also raised the question of how to
evaluate explanations of machine learning (ML) models. While interpretability
and explainability are often presented as a subjectively validated binary
property, we consider it a multi-faceted concept. We identify 12 conceptual
properties, such as Compactness and Correctness, that should be evaluated for
comprehensively assessing the quality of an explanation. Our so-called Co-12
properties serve as categorization scheme for systematically reviewing the
evaluation practice of more than 300 papers published in the last 7 years at
major AI and ML conferences that introduce an XAI method. We find that 1 in 3
papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate
with users. We also contribute to the call for objective, quantifiable
evaluation methods by presenting an extensive overview of quantitative XAI
evaluation methods. This systematic collection of evaluation methods provides
researchers and practitioners with concrete tools to thoroughly validate,
benchmark and compare new and existing XAI methods. This also opens up
opportunities to include quantitative metrics as optimization criteria during
model training in order to optimize for accuracy and interpretability
simultaneously.Comment: Link to website added: https://utwente-dmb.github.io/xai-papers
Machine Learning-Driven Decision Making based on Financial Time Series
L'abstract eÌ presente nell'allegato / the abstract is in the attachmen
A Data-driven Case-based Reasoning in Bankruptcy Prediction
There has been intensive research regarding machine learning models for
predicting bankruptcy in recent years. However, the lack of interpretability
limits their growth and practical implementation. This study proposes a
data-driven explainable case-based reasoning (CBR) system for bankruptcy
prediction. Empirical results from a comparative study show that the proposed
approach performs superior to existing, alternative CBR systems and is
competitive with state-of-the-art machine learning models. We also demonstrate
that the asymmetrical feature similarity comparison mechanism in the proposed
CBR system can effectively capture the asymmetrically distributed nature of
financial attributes, such as a few companies controlling more cash than the
majority, hence improving both the accuracy and explainability of predictions.
In addition, we delicately examine the explainability of the CBR system in the
decision-making process of bankruptcy prediction. While much research suggests
a trade-off between improving prediction accuracy and explainability, our
findings show a prospective research avenue in which an explainable model that
thoroughly incorporates data attributes by design can reconcile the dilemma
- âŠ