Nodes Of Tree Research Articles

The robust cyclic job shop problem

This paper deals with the cyclic job shop problem where the task durations are uncertain and belong to a polyhedral uncertainty set. We formulate the cyclic job shop problem as a two-stage robust optimization model. The cycle time and the execution order of tasks executed on the same machines correspond to the here-and-now decisions and have to be decided before the realization of the uncertainty. The starting times of tasks corresponding to the wait-and-see decisions are delayed and can be adjusted after the uncertain parameters are known. In the last decades, different solution approaches have been developed for two-stage robust optimization problems. Among them, the use of affine policies, row and row-and-column generation algorithms are the most common. In this paper, we propose a branch-and-bound algorithm to tackle the robust cyclic job shop problem with cycle time minimization. The algorithm uses, at each node of the search tree, a robust version of the Howard’s algorithm to derive a lower bound on the optimal cycle time. Moreover, we design a row generation algorithm and a column-and-row generation algorithm and compare it to the branch-and-bound method. Finally, encouraging preliminary results on numerical experiments performed on randomly generated instances are presented.

Efficiency analysis trees as a tool to analyze the quality of university education

<span lang="EN-US">This research aims to design a quality management tool in education. The methodology comprises two stages: first, the construction of the decision tree model, and second, the efficiency evaluation. For the validation and development of this research, the data modelled corresponds to the standardized exams for higher education in Colombia of ninety industrial engineering degrees. Among the results, the citizenship skills (CC_PRO) generate the most significant contribution to the model. On the other hand, the written communication competence (CE_PRO) generates a minor contribution to the model. In addition, the most relevant result of the research is the design and validation of a tool to estimate educational efficiency using the efficiency analysis tree (EAT) and data enveloping analysis (DEA) models. The proposed tool allows the generation of specific targets to increase the level of efficiency of universities through the nodes of the decision tree, which contributes to the spectrum of knowledge on models for educational management. In conclusion, this research presents a tool for the management of educational processes through the analysis of efficiency using EAT, estimating the efficiency of universities and setting the foundations for forecasting future efficiency scenarios.</span>

Design of reduced-current overcurrent protection circuit based on GBDT model

Abstract To address the problems of slow response and insufficient protection sensitivity of existing overcurrent protection techniques, this paper verifies the feasibility of the design of reduced-current overcurrent protection circuits by using the GBDT model. Firstly, the regression tree is used as a circuit learner for iteration, and the CART algorithm is applied to calculate circuit splitting characteristics and optimal splitting points to obtain the gain of splitting. Then the line loss rate is calculated using the differential evolution method to derive the objective function of the minimized line and the optimal value of the circuit. Finally, the mean value is extracted by calculating the optimal line’s statistical characteristics, the regression tree nodes are calculated using the GBDT model, and the root means the square error is used to verify the effectiveness of the circuit protection design proposed in this paper. The experimental results show that the overcurrent protection value of the circuit sample is around 1.35A and the current limit protection time is around 13ms by verifying the current reduction protection function and indication function of the circuit in the full temperature range. This shows that the GBDT model enables the reduced-current overcurrent protection circuit to perform timely and accurate overcurrent detection actions, and the circuit can be reliably protected under different overcurrent conditions.

Application of random forest algorithm in the detection of foreign objects in wine

Abstract In order to explore the applicability of random forest algorithm in the detection of alcoholic foreign matter and to improve the identification of alcoholic products. In this paper, based on the random forest algorithm, the feature values of random forest are fuzzified using the fuzzy comprehensive evaluation method, and the application model of alcohol foreign body detection anomaly based on random forest fuzzy tree nodes is established. And the reliability of the random forest algorithm is verified by the quantitative analysis of the three test indexes (i.e., recall, precision, and accuracy) and the anomaly proportion coefficient of the test data set by the algorithm in this paper. The results show that the recall, precision, and accuracy of the random forest-based anomaly detection for alcoholic beverage foreign objects are 99.65%, 95.49%, and 97.19%, respectively, and the average value of the three eigenvalues of this paper’s algorithm is 97.44%, which is 59.89%, 43.98%, and 1.92% higher than the other three algorithms, respectively. In terms of the anomaly proportion coefficient, the stability of the algorithm in this paper is the best when the coefficient takes values between [0.2, 0.6]. It can be shown that the algorithm based on random forest can be applied to the foreign matter detection of wine, and through the detection of anomalies, the quality of the wine currently undergoing detection can be clearly and explicitly analyzed, which also provides a new direction for the application of the random forest algorithm.

Congruence between ultraconserved element-based matrices and phylotranscriptomic datasets in the scorpion Tree of Life.

Scorpions are ancient and historically renowned for their potent venom. Traditionally, the systematics of this group of arthropods was supported by morphological characters, until recent phylogenomic analyses (using RNAseq data) revealed most of the higher-level taxa to be non-monophyletic. While these phylogenomic hypotheses are stable for almost all lineages, some nodes have been hard to resolve due to minimal taxonomic sampling (e.g. family Chactidae). In the same line, it has been shown that some nodes in the Arachnid Tree of Life show disagreement between hypotheses generated using transcritptomes and other genomic sources such as the ultraconserved elements (UCEs). Here, we compared the phylogenetic signal of transcriptomes vs. UCEs by retrieving UCEs from new and previously published scorpion transcriptomes and genomes, and reconstructed phylogenies using both datasets independently. We reexamined the monophyly and phylogenetic placement of Chactidae, sampling an additional chactid species using both datasets. Our results showed that both sets of genome-scale datasets recovered highly similar topologies, with Chactidae rendered paraphyletic owing to the placement of Nullibrotheas allenii. As a first step toward redressing the systematics of Chactidae, we establish the family Anuroctonidae (new family) to accommodate the genus Anuroctonus.

Beyond Pairwise Reasoning in Multi-Agent Path Finding

In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.

PM2VE: Power Metering Model for Virtualization Environments in Cloud Data Centers

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many practical models for estimating the power consumption of virtual machines (VMs) have been proposed, few power estimation models of containers have been put forth. In this paper, we propose a fast-training piecewise regression model based on a decision tree for VM power metering and estimate the power of containers configured on the VM by treating the container as a group of processes on the VM. We select appropriate features from the collected metrics of VMs/containers to help our model fit the nonlinear relationship between power and features well. Besides, we optimize the leaf nodes of the regression tree, realizing the effective power metering of virtualization environments. We evaluate the proposed model on 13 tasks in PARSEC and compare it with several commonly used models in data centers. The experimental results prove the effectiveness of the proposed model, and the estimated power of containers is in line with expectations.

Probabilistic Attribute Tree Structured Convolutional Neural Networks for Facial Expression Recognition in the Wild

Very recent work has demonstrated tremendous improvements in facial expression recognition (FER) using laboratory-controlled datasets. However, recognizing facial expressions under in-the-wild conditions still remains a challenge, especially on unseen subjects due to high inter-subject variations. In this paper, we propose a novel Probabilistic Attribute Tree Convolutional Neural Network (PAT-CNN) to explicitly deal with large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a PAT module with an associated PAT loss is proposed to learn features in a hierarchical tree structure organized according to identity-related attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. Furthermore, the proposed PAT-CNN can be learned from limited attribute-annotated samples to make the best use of available data. Experimental results on four spontaneous facial expression datasets, i.e., RAF-DB, SFEW, ExpW, and FER-2013, have demonstrated that the proposed PAT-CNN achieves the best performance when compared to state-of-the-art methods by explicitly modeling attributes. Impressively, a single model PAT-CNN achieves the best performance on the SFEW test dataset when compared to the state-of-the-art methods using an ensemble of hundreds of CNNs.

Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.

Fully-Dynamic Decision Trees

We develop the first fully dynamic algorithm that maintains a decision tree over an arbitrary sequence of insertions and deletions of labeled examples. Given ε>0 our algorithm guarantees that, at every point in time, every node of the decision tree uses a split with Gini gain within an additive ε of the optimum. For real-valued features the algorithm has an amortized running time per insertion/deletion of O((d·log³n)/ε²), which improves to O((d·log²n)/ε) for binary or categorical features, while it uses space O(n·d), where n is the maximum number of examples at any point in time and d is the number of features. Our algorithm is nearly optimal, as we show that any algorithm with similar guarantees requires amortized running time Ω(d) and space Ω(n·d/polylog(nd)). We complement our theoretical results with an extensive experimental evaluation on real-world data, showing the effectiveness of our algorithm.

BUILDING MINIMUM SPANNING TREES BY LIMITED NUMBER OF NODES OVER TRIANGULATED SET OF INITIAL NODES

Background. The common purpose of modelling and using minimum spanning trees is to ensure efficient coverage. In many tasks of designing efficient telecommunication networks, the number of network nodes is usually limited. In terms of rational allocation, there are more possible locations than factually active tools to be settled to those locations.
 Objective. Given an initial set of planar nodes, the problem is to build a minimum spanning tree connecting a given number of the nodes, which can be less than the cardinality of the initial set. The root node is primarily assigned, but it can be changed if needed.
 Methods. To obtain a set of edges, a Delaunay triangulation is performed over the initial set of nodes. Distances between every pair of the nodes in respective edges are calculated. These distances being the lengths of the respective edges are used as graph weights, and a minimum spanning tree is built over this graph.
 Results. The problem always has a solution if the desired number of nodes (the number of available recipient nodes) is equal to the number of initially given nodes. If the desired number is lesser, the maximal edge length is found and the edges of the maximal length are excluded while the number of minimum spanning tree nodes is greater than the desired number of nodes.
 Conclusions. To build a minimum spanning tree by a limited number of nodes it is suggested to use the Delaunay triangulation and an iterative procedure in order to meet the desired number of nodes. Planar nodes of an initial set are triangulated, whereupon the edge lengths are used as weights of a graph. The iterations to reduce nodes are done only if there are redundant nodes. When failed, the root node must be changed before the desired number of nodes is changed.

Multi-Label Classification of Chinese Rural Poverty Governance Texts Based on XLNet and Bi-LSTM Fused Hierarchical Attention Mechanism

Hierarchical multi-label text classification (HMTC) is a highly relevant and widely discussed topic in the era of big data, particularly for efficiently classifying extensive amounts of text data. This study proposes the HTMC-PGT framework for poverty governance’s single-path hierarchical multi-label classification problem. The framework simplifies the HMTC problem into training and combination problems of multi-class classifiers in the classifier tree. Each independent classifier in this framework uses an XLNet pretrained model to extract char-level semantic embeddings of text and employs a hierarchical attention mechanism integrated with Bi-LSTM (BiLSTM + HA) to extract semantic embeddings at the document level for classification purposes. Simultaneously, this study proposes that the structure uses transfer learning (TL) between classifiers in the classifier tree. The experimental results show that the proposed XLNet + BiLSTM + HA + FC + TL model achieves micro-P, micro-R, and micro-F1 values of 96.1%, which is 7.5~38.1% higher than those of other baseline models. The HTMC-PGT framework based on XLNet, BiLSTM + HA, and transfer learning (TL) between classifier tree nodes proposed in this study solves the hierarchical multi-label classification problem of poverty governance text (PGT). It provides a new idea for solving the traditional HMTC problem.

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.

Phylogenomic resolution of Imparidentia (Mollusca: Bivalvia) diversification through mitochondrial genomes.

The online version contains supplementary material available at 10.1007/s42995-023-00178-x.

Prediction of rhinitis based on label-links classifier chain using feature-block

Rhinitis is a common chronic inflammation of the upper respiratory tract with multiple symptoms and signs. The clinical classification of rhinitis is a multi-label classification, characterized by high dimensionality, poor correlation and class imbalance. Low recognition rate and poor generalization performance often occur for minority class instances. Therefore, this paper proposes a Feature-Block classification model, MBLCC based on Label-Links Classifier Chain. We apply kernel density estimation and Deborah Hellinger distance to partition allergic rhinitis instance with similar characteristics, calculate the correlation matrix of label characteristics, build an ordered classification chain for each block, and integrate the predictions of each block classifiers by evidence theory as the outcome. The cross-validation experiments conducted on 2231 cases of clinical rhinitis show that the evaluation indicators of MBLCC, i.e. sensitivity, specificity, accuracy, F1-score, and G-Mean, are 91.80 %, 96.8 %, 96.9 %, 0.925, and 0.941 respectively. In comparison with the other baselines, MBLCC achieves better generalization performance and is more effective and rapid in early clinical diagnosis of rhinitis. In addition, we calculate the feature importance ranking for rhinitis features via Label-Links Classifier Chain on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classification that can provides the reference for the clinical rhinitis diagnosis.

Power user portrait model based on random forest

AbstractWith the vigorous development of the energy Internet, all kinds of user information data are increasing day by day. How to comprehensively and deeply mine the effective information of users, develop a model to predict the behaviour characteristics of big data users, distinguish customer relationships, and provide an accurate basis for the next behaviour of users for various platforms have become one of the research hotspots of big data analysis of user behaviour. The data is sampled according to the feature vector of power user. The portrait mining of power user is conducted, and the user screening and analysis are conducted by using the measure of decision tree node purity in the model. The decision tree variable of the up–down stopping rule is generated. Then the results of the model and the Logistics model are tested and analysed, which can effectively predict the behaviour of power user. The proposed user strategy based on the characteristics of power consumption behaviour is analysed to verify the effectiveness of the scheme. The example shows that the model has a strong ability to distinguish and good stability than the traditional Logistics model, which can effectively predict the user's behaviour in advance, reduce user complaints, and help enterprises and users to form a long‐term mechanism of mutual benefit and reciprocity, which has a strong practical significance. This paper analyses the panorama of users through power big data technology and proposes a maturity model to evaluate the priority of users' electricity consumption. It emphasises the use of resources and methods provided in the power big data technology package to solve the practical problems of users' electricity consumption, and helps power companies to avoid market risks and improve service levels, which has strong practical significance.

MetaDT: Meta Decision Tree With Class Hierarchy for Interpretable Few-Shot Learning

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-learning and representation learning. However, few works focus on the interpretability of FSL decision process. In this paper, we take a step towards the interpretable FSL by proposing a novel meta-learning based decision tree framework, namely, MetaDT. In particular, the FSL interpretability is achieved from two aspects, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., a concept aspect and a visual aspect. On the concept aspect, we first introduce a tree-like concept hierarchy as FSL prior. Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree. The advantage of such design is that a sequence of high-level concept decisions that lead up to a final class prediction can be obtained, which clarifies the FSL decision process. On the visual aspect, a set of subtask-specific classifiers with visual attention mechanism is designed to perform decision at each node of the decision tree. As a result, a subtask-specific heatmap visualization can be obtained to achieve the decision interpretability of each tree node. At last, to alleviate the data scarcity issue of FSL, we regard the prior of concept hierarchy as an undirected graph, and then design a graph convolution-based decision tree inference network as our meta-learner to infer parameters of the decision tree. Extensive experiments on performance comparison and interpretability analysis show superiority of our MetaDT.

When Tree Meets Hash: Reducing Random Reads for Index Structures on Persistent Memories

Indexing structures are widely used in modern data-processing applications to support high-performance queries, and there are a variety of recent designs specifically optimized for the newly available persistent memory (PM). The primary focus of previous PM indexes is on reducing the expensive PM writes for persisting data. However, we find that in tree-based PM indexes, because of the smaller performance gap between writes and random reads on real PM devices, the read-intensive tree traversal phase dominates the overall latency. This observation calls for further optimizations on existing indexing structures for PM. In this paper, we propose Extendible Radix Tree (ERT), an efficient indexing structure for PM that significantly reduces tree heights to minimize random reads, while still maintaining fast in-node search speed. The key idea is to use extendible hashing for each node in a radix tree. This design allows us to have a relatively large fanout of the radix tree to keep the tree height small, and also to realize constant-time lookups within a node. Using extendible hashing also allows for incremental node modification without excessive writes during inserts and updates. Range queries are efficiently and robustly handled by enforcing partial ordering among the keys in the hash table of each node without introducing more hash collisions. Our experiments on both synthetic and real-world data sets demonstrate that ERT achieves up to 2.65×, 4.41×, and 2.43× speedups for search, insert, and range queries over the respectively state-of-the-art PM index.

Recovering hierarchies in terms of content similarity

Several real-world and abstract structures and systems are characterized by marked hierarchy to the point of being expressed as trees. Since the study of these entities often involves sampling (or discovering) the tree nodes in a specific order that may not correspond to the original shape of the tree, reconstruction errors can be obtained. The present work addresses this important problem based on two main resources: (i) the adoption of a simple model of trees, involving a single parameter; and (ii) the use of the coincidence similarity as the means to quantify the errors by comparing the original and reconstructed structures considering the effects of hierarchical structure, nodes content, and uncertainty. Several interesting results are described and discussed, including that the accuracy of hierarchical reconstructions is highly dependent on the values of the uncertainty parameter as well as on the types of trees and that changes in the value of the content parameter can affect the accuracy of reconstructing hierarchies.

Virus Pop-Expanding Viral Databases by Protein Sequence Simulation.

The improvement of our knowledge of the virosphere, which includes unknown viruses, is a key area in virology. Metagenomics tools, which perform taxonomic assignation from high throughput sequencing datasets, are generally evaluated with datasets derived from biological samples or in silico spiked samples containing known viral sequences present in public databases, resulting in the inability to evaluate the capacity of these tools to detect novel or distant viruses. Simulating realistic evolutionary directions is therefore key to benchmark and improve these tools. Additionally, expanding current databases with realistic simulated sequences can improve the capacity of alignment-based searching strategies for finding distant viruses, which could lead to a better characterization of the "dark matter" of metagenomics data. Here, we present Virus Pop, a novel pipeline for simulating realistic protein sequences and adding new branches to a protein phylogenetic tree. The tool generates simulated sequences with substitution rate variations that are dependent on protein domains and inferred from the input dataset, allowing for a realistic representation of protein evolution. The pipeline also infers ancestral sequences corresponding to multiple internal nodes of the input data phylogenetic tree, enabling new sequences to be inserted at various points of interest in the group studied. We demonstrated that Virus Pop produces simulated sequences that closely match the structural and functional characteristics of real protein sequences, taking as an example the spike protein of sarbecoviruses. Virus Pop also succeeded at creating sequences that resemble real sequences not included in the databases, which facilitated the identification of a novel pathogenic human circovirus not included in the input database. In conclusion, Virus Pop is helpful for challenging taxonomic assignation tools and could help improve databases to better detect distant viruses.