144 research outputs found
PredDiff: Explanations and Interactions from Conditional Expectations
PredDiff is a model-agnostic, local attribution method that is firmly rooted
in probability theory. Its simple intuition is to measure prediction changes
while marginalizing features. In this work, we clarify properties of PredDiff
and its connection to Shapley values. We stress important differences between
classification and regression, which require a specific treatment within both
formalisms. We extend PredDiff by introducing a new, well-founded measure for
interaction effects between arbitrary feature subsets. The study of interaction
effects represents an inevitable step towards a comprehensive understanding of
black-box models and is particularly important for science applications. As
opposed to Shapley values, our novel measure maintains the original linear
scaling and is thus generally applicable to real-world problems.Comment: 28 pages, 13 Figures, major revision (completeness relation, new
experiments, comparison to Shapley values), code available at
https://github.com/PredDiff/PredDiff202
Expanding Explainability Horizons: A Unified Concept-Based System for Local, Global, and Misclassification Explanations
Explainability of intelligent models has been garnering increasing attention
in recent years. Of the various explainability approaches, concept-based
techniques are notable for utilizing a set of human-meaningful concepts instead
of focusing on individual pixels. However, there is a scarcity of methods that
consistently provide both local and global explanations. Moreover, most of the
methods have no offer to explain misclassification cases. To address these
challenges, our study follows a straightforward yet effective approach. We
propose a unified concept-based system, which inputs a number of
super-pixelated images into the networks, allowing them to learn better
representations of the target's objects as well as the target's concepts. This
method automatically learns, scores, and extracts local and global concepts.
Our experiments revealed that, in addition to enhancing performance, the models
could provide deeper insights into predictions and elucidate false
classifications
Target classification in multimodal video
The presented thesis focuses on enhancing scene segmentation and target recognition methodologies via the mobilisation of contextual information. The algorithms developed to achieve this goal utilise multi-modal sensor information collected across varying scenarios,
from controlled indoor sequences to challenging rural locations. Sensors are chiefly colour band and long wave infrared (LWIR), enabling persistent surveillance capabilities across all environments. In the drive to develop effectual algorithms towards the outlined goals, key obstacles are identified and examined: the recovery of background scene structure from foreground object ’clutter’, employing contextual foreground knowledge to circumvent training a classifier when labeled data is not readily available, creating a labeled LWIR dataset to train a convolutional neural network (CNN) based object classifier and the viability of spatial context to address long range target classification when big data solutions are not enough. For an environment displaying frequent foreground clutter, such as a busy train station, we propose an algorithm exploiting foreground object presence to segment underlying scene structure that is not often visible. If such a location is outdoors and surveyed by an infra-red (IR) and visible band camera set-up, scene context and contextual knowledge transfer allows reasonable class predictions for thermal signatures within the scene to be determined. Furthermore, a labeled LWIR image corpus is created to train an infrared object classifier, using a CNN approach. The trained network demonstrates effective classification accuracy of 95% over 6 object classes. However, performance is not sustainable for IR targets acquired at long range due to low signal quality and classification accuracy drops. This is addressed by mobilising spatial context to affect network class scores, restoring robust classification capability
Explainable Information Retrieval using Deep Learning for Medical images
Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Explainable Artificial Intelligence (XAI) module has been utilised to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods
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