Decision trees are traditionally trained using greedy heuristics that locally optimize an impurity or information metric. Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly. We identify two relatively unexplored aspects of ODTs: the objective function used in training trees and tuning techniques. Additionally, the value of optimal methods is not well understood yet, as the literature provides conflicting results, with some demonstrating superior out-of-sample performance of ODTs over greedy approaches, while others show the exact opposite. In this paper, we address these three questions: what objective to optimize in ODTs; how to tune ODTs; and how do optimal and greedy methods compare? Our experimental evaluation examines 13 objective functions, including four novel objectives resulting from our analysis, seven tuning methods, and six claims from the literature on optimal and greedy methods on 165 real and synthetic data sets. Through our analysis, both conceptually and experimentally, we discover new non-concave objectives, highlight the importance of proper tuning, support and refute several claims from the literature, and provide clear recommendations for researchers and practitioners on the usage of greedy and optimal methods, and code for future comparisons.

This work introduces a novel control-aware distributed process monitoring methodology based on modules comprised of clusters of interacting measurements. The methodology relies on the process flow diagram (PFD) and control system structure without requiring cross-correlation data to create monitoring modules. The methodology is validated on the Tennessee Eastman Process benchmark using full Principal Component Analysis (f-PCA) in the monitoring modules. The results are comparable to nonlinear techniques implemented in a centralized manner such as Kernel PCA (KPCA), Autoencoders (AE), and Recurrent Neural Networks (RNN), or distributed techniques like the Distributed Canonical Correlation Analysis (DCCA). Temporal plots of fault detection by different modules show clearly the magnitude and propagation of the fault through each module, pinpointing the module where the fault originates, and separating controllable faults from other faults. This information, combined with PCA contribution plots, helps detection and identification as effectively as more complex nonlinear centralized or distributed methods.

This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning capabilities for the task.We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the critical role of the knowledge injection module,as the removal of this module resulted in a significant drop in F1 score. The experimental results demonstrate that the proposed method not only effectively improves the accuracy of disease diagnosis but also enhances the interpretability of the predictions, providing more reliable support and evidence for clinical diagnosis.

Label noise refers to the phenomenon where instances in a data set are assigned to the wrong label. Label noise is harmful to classifier performance, increases model complexity and impairs feature selection. Addressing label noise is crucial, yet current research primarily focuses on image and text data using deep neural networks. This leaves a gap in the study of tabular data and gradient-boosted decision trees (GBDTs), the leading algorithm for tabular data. Different methods have already been developed which either try to filter label noise, model label noise while simultaneously training a classifier or use learning algorithms which remain effective even if label noise is present. This study aims to further investigate the effects of label noise on gradient-boosted decision trees and methods to mitigate those effects. Through comprehensive experiments and analysis, the implemented methods demonstrate state-of-the-art noise detection performance on the Adult dataset and achieve the highest classification precision and recall on the Adult and Breast Cancer datasets, respectively. In summary, this paper enhances the understanding of the impact of label noise on GBDTs and lays the groundwork for future research in noise detection and correction methods.

Diagnostic reasoning is a key component of expert work in many domains. It is a hard, time-consuming activity that requires expertise, and AI research has investigated the ways automated systems can support this process. Yet, due to the complexity of natural language, the applications of AI for diagnostic reasoning to language-related tasks are lacking. To close this gap, we investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR). We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models and instantiate it in a comprehensive study of scientific paper assessment in the biomedical domain. We use the resulting dataset to investigate the human decision-making process in NL-DAR and determine the potential of LLMs to support structured decision-making over text. Our framework, open resources and tools lay the groundwork for the empirical study of collaborative diagnostic reasoning in the age of LLMs, in the scholarly domain and beyond.

Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with sociolinguistics experts to label spoofed audio samples with Expert Defined Linguistic Features (EDLFs) that can be discerned by the human ear: pitch, pause, word-initial and word-final release bursts of consonant stops, audible intake or outtake of breath, and overall audio quality. It is established that there is an improvement in several deepfake detection algorithms when they augmented the traditional and common features of audio data with these EDLFs. In this paper, using a hybrid dataset comprised of multiple types of spoofed audio augmented with sociolinguistic annotations, we investigate causal discovery and inferences between the discernible linguistic features and the label in the audio clips, comparing the findings of the causal models with the expert ground truth validation labeling process. Our findings suggest that the causal models indicate the utility of incorporating linguistic features to help discern spoofed audio, as well as the overall need and opportunity to incorporate human knowledge into models and techniques for strengthening AI models. The causal discovery and inference can be used as a foundation of training humans to discern spoofed audio as well as automating EDLFs labeling for the purpose of performance improvement of the common AI-based spoofed audio detectors.

Decision trees, owing to their interpretability, are attractive as control policies for (dynamical) systems. Unfortunately, constructing, or synthesising, such policies is a challenging task. Previous approaches do so by imitating a neural-network policy, approximating a tabular policy obtained via formal synthesis, employing reinforcement learning, or modelling the problem as a mixed-integer linear program. However, these works may require access to a hard-to-obtain accurate policy or a formal model of the environment (within reach of formal synthesis), and may not provide guarantees on the quality or size of the final tree policy. In contrast, we present an approach to synthesise optimal decision-tree policies given a black-box environment and specification, and a discretisation of the tree predicates, where optimality is defined with respect to the number of steps to achieve the goal. Our approach is a specialised search algorithm which systematically explores the (exponentially large) space of decision trees under the given discretisation. The key component is a novel pruning mechanism that significantly reduces the search space. Our approach represents a conceptually novel way of synthesising small decision-tree policies with optimality guarantees even for black-box environments with black-box specifications.

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference but also lacking the explainability. To address these issues, we propose a diffusion-based weakly-supervised approach for deriving health indicators of rotating machines, enabling early fault detection and continuous monitoring of condition evolution. This approach relies on a classifier-free diffusion model trained using healthy samples and a few anomalies. This model generates healthy samples. and by comparing the differences between the original samples and the generated ones in the envelope spectrum, we construct an anomaly map that clearly identifies faults. Health indicators are then derived, which can explain the fault types and mitigate noise interference. Comparative studies on two cases demonstrate that the proposed method offers superior health monitoring effectiveness and robustness compared to baseline models.

The escalating global mortality and morbidity rates associated with gastrointestinal (GI) bleeding, compounded by the complexities and limitations of traditional endoscopic methods, underscore the urgent need for a critical review of current methodologies used for addressing this condition. With an estimated 300,000 annual deaths worldwide, the demand for innovative diagnostic and therapeutic strategies is paramount. The introduction of Video Capsule Endoscopy (VCE) has marked a significant advancement, offering a comprehensive, non-invasive visualization of the digestive tract that is pivotal for detecting bleeding sources unattainable by traditional methods. Despite its benefits, the efficacy of VCE is hindered by diagnostic challenges, including time-consuming analysis and susceptibility to human error. This backdrop sets the stage for exploring Machine Learning (ML) applications in automating GI bleeding detection within capsule endoscopy, aiming to enhance diagnostic accuracy, reduce manual labor, and improve patient outcomes. Through an exhaustive analysis of 113 papers published between 2008 and 2023, this review assesses the current state of ML methodologies in bleeding detection, highlighting their effectiveness, challenges, and prospective directions. It contributes an in-depth examination of AI techniques in VCE frame analysis, offering insights into open-source datasets, mathematical performance metrics, and technique categorization. The paper sets a foundation for future research to overcome existing challenges, advancing gastrointestinal diagnostics through interdisciplinary collaboration and innovation in ML applications.

In the realm of dermatology, the complexity of diagnosing skin conditions manually necessitates the expertise of dermatologists. Accurate identification of various skin ailments, ranging from cancer to inflammatory diseases, is paramount. However, existing artificial intelligence (AI) models in dermatology face challenges, particularly in accurately diagnosing diseases across diverse skin tones, with a notable performance gap in darker skin. Additionally, the scarcity of publicly available, unbiased datasets hampers the development of inclusive AI diagnostic tools. To tackle the challenges in accurately predicting skin conditions across diverse skin tones, we employ a transfer-learning approach that capitalizes on the rich, transferable knowledge from various image domains. Our method integrates multiple pre-trained models from a wide range of sources, including general and specific medical images, to improve the robustness and inclusiveness of the skin condition predictions. We rigorously evaluated the effectiveness of these models using the Diverse Dermatology Images (DDI) dataset, which uniquely encompasses both underrepresented and common skin tones, making it an ideal benchmark for assessing our approach. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, we conducted domain adaptation using additional skin image datasets such as HAM10000. This adaptation significantly improved model performance across all models.