Tree Kernel Research Articles

Clarity in complexity: how aggregating explanations resolves the disagreement problem

The Rashômon Effect, applied in Explainable Machine Learning, refers to the disagreement between the explanations provided by various attribution explainers and to the dissimilarity across multiple explanations generated by a particular explainer for a single instance from the dataset (differences between feature importances and their associated signs and ranks), an undesirable outcome especially in sensitive domains such as healthcare or finance. We propose a method inspired from textual-case based reasoning for aligning explanations from various explainers in order to resolve the disagreement and dissimilarity problems. We iteratively generated a number of 100 explanations for each instance from six popular datasets, using three prevalent feature attribution explainers: LIME, Anchors and SHAP (with the variations Tree SHAP and Kernel SHAP) and consequently applied a global cluster-based aggregation strategy that quantifies alignment and reveals similarities and associations between explanations. We evaluated our method by weighting the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:k$$\\end{document}-NN algorithm with agreed feature overlap explanation weights and compared it to a non-weighted \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:k$$\\end{document}-NN predictor, having as task binary classification. Also, we compared the results of the weighted \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:k$$\\end{document}-NN algorithm using aggregated feature overlap explanation weights to the weighted \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:k$$\\end{document}-NN algorithm using weights produced by a single explanation method (either LIME, SHAP or Anchors). Our global alignment method benefited the most from a hybridization with feature importance scores (information gain), that was essential for acquiring a more accurate estimate of disagreement, for enabling explainers to reach a consensus across multiple explanations and for supporting effective model learning through improved classification performance.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes.

Detection of network false information based on artificial intelligence models

There is often some false information in social platforms to mislead public opinion. Due to the rapid development of the Internet, the spread of false information on the Internet has become easier, which has brought many losses to people's economy and life. In this paper, the relevant research on false information based on bidirectional convolutional networks is analyzed, and the method is divided into four stages: data preprocessing, model architecture, training process and prediction process. Then, the relevant research on rumor detection by propagation tree kernel model are analyzed. Finally, this paper delineates a comprehensive framework that amalgamates enhanced Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybridized Black Widow Optimization (BWO) with Moth Optimization Algorithm (MOA) (referred to as HM-BWO) for the accurate detection of network-based false information. This paper analyzes and discusses the challenges of poor universality and insufficient detection speed encountered by the current rumor detection research and puts forward the idea of introducing migration model to solve the problem of poor universality and Spark to solve the problem of insufficient detection speed. This article provides a good overview of the field of online disinformation.

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis.

Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.

Physico-chemical and nutritional characteristics of kernels oil from two mangoes varieties (Amélie and Kent) harvested at Orodara in Burkina Faso

The mango tree (Mangifera indica) plays an important role for Burkina Faso people. This importance is due to its consumption, its pharmaceutical properties and its shade. However, all this importance is limited to the fleshy parts, leaves, bark and roots, whereas the mango tree's kernels are full of potential that can be exploited, but remain little known. Two mango varieties (Amélie and Kent) were sampled at the Bobo-Dioulasso fruit and vegetable market from Orodara. The fat content and physico-chemical parameters of the mangoes were determined using conventional methods. The results obtained show that the oil's biochemical composition gives it its full importance. This oil meets most of the standards set by the Codex Alimentarius for virgin oils. Mango kernels do indeed contain a fat content of between 3.039 and 6.486%. For moisture content, we found 56.095% and 56.070%, which already exceeds half the kernel's constitutional weight. In terms of chemical parameters, the highest acid value was 2.664 mg KOH/g, compared with 4.0 mg KOH/g as the maximum limit of the Codex Alimentarius standard. The peroxide value was equal to 9.523 mEqO₂/kg against 10 mEqO₂/kg as the maximum limit of the standard. We have a good concentration of saturated fatty acids at least 47% for the lowest concentration and 24% of unsaturated fatty acids for the lowest concentration. In view of these results, mango kernels should be valorized in order to add value to mango waste, with a view to use in cosmetics or medicine, as well as to depollute the environment.

Regression test prioritization leveraging source code similarity with tree kernels

AbstractRegression test prioritization (RTP) is an active research field, aiming at re‐ordering the tests in a test suite to maximize the rate at which faults are detected. A number of RTP strategies have been proposed, leveraging different factors to reorder tests. Some techniques include an analysis of changed source code, to assign higher priority to tests stressing modified parts of the codebase. Still, most of these change‐based solutions focus on simple text‐level comparisons among versions. We believe that measuring source code changes in a more refined way, capable of discriminating between mere textual changes (e.g., renaming of a local variable) and more structural changes (e.g., changes in the control flow), could lead to significant benefits in RTP, under the assumption that major structural changes are also more likely to introduce faults. To this end, we propose two novel RTP techniques that leverage tree kernels (TK), a class of similarity functions largely used in Natural Language Processing on tree‐structured data. In particular, we apply TKs to abstract syntax trees of source code, to more precisely quantify the extent of structural changes in the source code, and prioritize tests accordingly. We assessed the effectiveness of the proposals by conducting an empirical study on five real‐world Java projects, also used in a number of RTP‐related papers. We automatically generated, for each considered pair of software versions (i.e., old version, new version) in the evolution of the involved projects, 100 variations with artificially injected faults, leading to over 5k different software evolution scenarios overall. We compared the proposed prioritization approaches against well‐known prioritization techniques, evaluating both their effectiveness and their execution times. Our findings show that leveraging more refined code change analysis techniques to quantify the extent of changes in source code can lead to relevant improvements in prioritization effectiveness, while typically introducing negligible overheads due to their execution.

Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics

This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.

Paraphrasing identification Using ACV-tree kernel

The aim of paraphrasing identification techniques is to identify if two sentences or texts have the same meaning; even if they do not contain a number of identical phrases or words. In this paper we proposed a hybrid technique based on using attention constituency vector (ACV)-tree kernel in short texts or sentence similarity computation. Then a similarity threshold is used to identifying if these sentences are paraphrased or not. The experiments are conducted on Arabic paraphrasing benchmark. The proposed method provides a recall of 70% and a precision of 76%, when the determined threshold is 0.5, while a recall of 94% and a precision of 0.751 achieved using a threshold of 0.3.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Abstract Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

Design of a Biogas Power Plant That Uses Olive Tree Pruning and Olive Kernels in Achaia, Western Greece

In Greece, agricultural residues form a significant part of available biomass resources. This study focuses on exploring energy production potential from olive tree pruning and kernels via anaerobic digestion in the Achaia region of Western Greece. It aims to address environmental challenges by analyzing anaerobic digestion of these residues. The study evaluates qualitative and quantitative attributes, including composition analysis and energy content assessment. Detailed design considerations for an anaerobic digestion system tailored for these residues are presented, laying the groundwork for practical implementation. By integrating scientific analysis with engineering principles, this research aims to optimize anaerobic digestion systems for a more sustainable agricultural landscape in Greece.

Method of Training a Kernel Tree

Method of Training a Kernel Tree

A novel diagnosis method for schizophrenia based on globus pallidus data

This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.

A Short-Text Similarity Model Combining Semantic and Syntactic Information

As one of the prominent research directions in the field of natural language processing (NLP), short-text similarity has been widely used in search recommendation and question-and-answer systems. Most of the existing short textual similarity models focus on considering semantic similarity while overlooking the importance of syntactic similarity. In this paper, we first propose an enhanced knowledge language representation model based on graph convolutional networks (KEBERT-GCN), which effectively uses fine-grained word relations in the knowledge base to assess semantic similarity and model the relationship between knowledge structure and text structure. To fully leverage the syntactic information of sentences, we also propose a computational model of constituency parse trees based on tree kernels (CPT-TK), which combines syntactic information, semantic features, and attentional weighting mechanisms to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates both semantic and syntactic information to comprehensively evaluate short-text similarity. The experimental results demonstrate that our proposed short-text similarity model outperforms the models proposed in recent years, achieving a Pearson correlation coefficient of 0.8805 on the STS-B dataset.

Bench-scale integrated bone and biochar bed treatment of geogenic fluoride contaminated groundwater from Bongo in Ghana

The article explores the adsorption of fluoride in groundwater and the synergistic properties of a mixture of bone char and biochar. The bone char and biochar were produced using cattle bones and coconut husks, bamboo, neem tree and palm kernel in a locally constructed double-barrel retort with particle size ranging from 0.05 to 0.315 mm. The morphology of the char was examined using a scanning electron microscope and energy dispersive spectroscopy for elements. The surface functional groups were assessed by Fourier-transform infrared spectroscopy. Almost all the fluoride was removed from the groundwater within 15–27 min of column treatment. Cattle bone char standalone treatment was the most effective adsorbent, with a removal rate of 91.4%, which is almost identical to the integrated removal rate of 91.1% for bone char and coconut husk biochar. The presence of the hydroxyapatite groups in the bone char together with the high surface areas of the chars increased the integrated bed's effectiveness in lowering fluoride concentrations in contaminated groundwater from 3.6 mg/L to 1.5 mg/L with an adsorption capacity of 0.084 mg/g. The breakthrough curves were predicted with the bed depth service time model under the same operational conditions. The integrated bed showed almost 100% efficacy in removing fluoride from contaminated groundwater with a 3.6 mg/L fluoride concentration within a breakthrough time of 15–27 min. This was due to the interplay between the chars' high surface areas and the hydroxyapatite groups in the bone char, which enhance adsorption and the synergistic effect of the integrated char.

A Closer Look at the Kernels Generated by the Decision and Regression Tree Ensembles

Tree ensembles can be interpreted as implicit kernel generators, where the ensuing proximity matrix represents the data-driven tree ensemble kernel. Focus of our work is the utility of tree based ensembles as kernel generators that (in conjunction with a regularized linear model) enable kernel learning. We elucidate the performance of the tree based random forest (RF) and gradient boosted tree (GBT) kernels in a comprehensive simulation study comprising of continuous and binary targets. We show that for continuous targets (regression), this kernel learning approach is competitive to the respective tree ensemble in higher dimensional scenarios, particularly in cases with larger number of noisy features. For the binary target (classification), the tree ensemble based kernels and their respective ensembles exhibit comparable performance. We provide the results from several real life datasets for regression and classification relevant for biopharmaceutical and biomedical applications, that are in line with the simulations to show how these insights may be leveraged in practice. We discuss general applicability and extensions of the tree ensemble based kernels for survival targets and interpretable landmarking in classification and regression. Finally, we outline future research for kernel learning due to feature space partitionings.

Colour Fastness of Silk Fabrics Dyed with Extracts from Oil Palm Tree Kernel Shell and Effect of Metal Ion Mordanting on Fabric Colour

Colour Fastness of Silk Fabrics Dyed with Extracts from Oil Palm Tree Kernel Shell and Effect of Metal Ion Mordanting on Fabric Colour

A Robust Framework for Automated Screening of Diabetic Patient Using ECG Signals

Diabetes mellitus (DM) or diabetes is an incurable, chronic, and genetic link health problem that occurs due to the higher glucose level in the blood. Continuous monitoring and automatic screening of diabetic patients will significantly improve the quality of the medical management system. DM induces cardiovascular autonomic neuropathy, which alters the morphology of electrocardiogram (ECG) signals. Hence, in this experiment, a new single-lead ECG signal database of 86 subjects (35 diabetic and 51 normal) is recorded. For the automatic screening of DM, an intrinsic time-scale decomposition (ITD) and machine learning-based framework is developed. In the first stage, denoised recorded signals are segmented into fragments of 5-s and decomposed into rotational components using the ITD algorithm. In the second stage, four features, namely, Hjorth complexity, Shannon entropy, log energy entropy, and log energy, are extracted from the ITD components. In the third stage, the Kruskal–Wallis (K–W) test is applied to select the most distinguishable features and fed to a decision tree classifier (DTC) with three different kernel functions for the automatic detection of diabetic patients. The fine tree (FT) kernel function provides the highest classification accuracy (ACC) of 86.9%. The proposed framework is developed using a 10-fold validation strategy. The developed framework is patient-centered suitable for screening in resource-limited environments and ready to be tested with more databases.

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function.

This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.

An unsupervised semantic text similarity measurement model in resource-limited scenes

As the basis of many artificial intelligence tasks, text similarity measurement has received extensive attention in current studies. However, few of them focus on the resource-limited scenes (i.e., limited computational resources and few training datasets), which are becoming increasingly popular and challenging with the development of the Internet of Things. Worse still, popular methods such as the deep-neural-network-based methods may lose their power in such scenes, since they typically require considerable computational resources. As for most current traditional methods, they also have issues of not effectively exploiting the semantic information in the sentences. As an alternative, this paper proposes a lightweight and semantically rich text similarity measurement model named the TES-TK model. In this model, a sentence is first transformed into a tree structure called TES-Tree with the integration of syntactic information, semantic knowledge, and topic distribution, aiming to comprehensively represent the multidimensional semantics of sentences. Afterward, a modified tree kernel model is designed to calculate the similarity between each pair of TES-Trees. In this way, the similarity score between the two related sentences can be retrieved. Experiments on 19 public benchmark datasets (STS2012–2015) demonstrate that the proposed approach exhibits significantly better performance than the compared eight peer methods on most datasets. Especially in resource-limited scenes, our approach achieved highly competitive results compared with the latest methods, such as BERT.

Gradient Boosting over Linguistic-Pattern-Structured Trees for Learning Protein–Protein Interaction in the Biomedical Literature

Protein-based studies contribute significantly to gathering functional information about biological systems; therefore, the protein–protein interaction detection task is one of the most researched topics in the biomedical literature. To this end, many state-of-the-art systems using syntactic tree kernels (TK) and deep learning have been developed. However, these models are computationally complex and have limited learning interpretability. In this paper, we introduce a linguistic-pattern-representation-based Gradient-Tree Boosting model, i.e., LpGBoost. It uses linguistic patterns to optimize and generate semantically relevant representation vectors for learning over the gradient-tree boosting. The patterns are learned via unsupervised modeling by clustering invariant semantic features. These linguistic representations are semi-interpretable with rich semantic knowledge, and owing to their shallow representation, they are also computationally less expensive. Our experiments with six protein–protein interaction (PPI) corpora demonstrate that LpGBoost outperforms the SOTA tree-kernel models, as well as the CNN-based interaction detection studies for BioInfer and AIMed corpora.