Search Results

Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially involves frequent ...

The time complexity during inference with a classically implemented binary decision tree (BDT) is stochastic and bound by depth. The lower and upper bounds depend on the shortest and tallest height of leaf node, respectively. This stochastic ...

Tree models have made an impressive progress during the past years, while an important problem is to understand how these models predict, in particular for critical applications such as finance and medicine. For this issue, most previous works ...

Deep reinforcement learning (DRL) has garnered remarkable success across various domains, propelled by advancements in deep learning (DL) technologies. However, the opacity of DL presents significant challenges, limiting the application of DRL in critical ...

We investigate the problem of private decision tree evaluation, which involves a server who holds a private decision tree, and a client who wants to classify its private attribute vector on the decision tree. The goal is to enable the client to learn the ...

Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved ...

• Various machine learning algorithms predicted esports match results with high accuracy from pre-match EEG data.
• LightGBM predicted esports match results with the highest accuracy of 80% from pre-match EEG data.
• EEG-MLs consistently ...

As machine learning (ML) systems are increasingly used in various fields, including tasks with high social impact, concerns about their fairness are growing. To address these concerns, individual fairness testing (IFT) has been introduced to identify ...

The evolution of recommendation systems has revolutionized user experiences by providing personalized recommendations. Although conventional systems such as collaborative and content-based filtering are reliable, they still suffer from inherent ...

This study proposes the BBQ-Tree, a new logic-based classifier that combines the two concepts of classical Decision Trees and Quantum-Logic Decision Trees into a generalized model. It thus creates a method that has the power to solve ...

Gradient Boosting is a leading learning method that builds ensembles and adapts their sizes to particular tasks, consistently delivering top-tier results across various applications. However, determining the optimal number of models in the ensemble ...

We introduce a hybrid machine learning algorithm that utilizes Genetic Programming-based Symbolic Regression (SR) to create decision trees (DT) with enhanced prediction, explainability, inference and robustness. Conventional DT algorithms for ...

More and more scenarios rely today on data analysis of massive amount of data, possibly contributed from multiple parties (data controllers). Data may, however, contain information that is sensitive or that should be protected (e.g., since it ...

Prior work has addressed the problem of confidential inference in decision trees. Both traditional order-preserving cryptography (OPE) and order-preserving NTRU cryptography have been used to ensure data and model privacy in decision trees. Furthermore, ...

The employment information of university graduates contains a lot of useful information that can provide guidance for employment. This paper studied the decision tree method in data mining and improved C4.5 with Taylor’s median theorem in order ...

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced ...

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This ...

The size of network data increasing tremendously day by day. This huge amount of data contains large number of attributes which need to be analysed for particular application. In this paper, three feature reduction techniques such as, principal component ...

Erythemato-squamous Diseases (ESD) encompass a group of common skin conditions, including psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. These dermatological conditions affect a ...

Modern cloud, web and other emerging distributed services have complex network requirements that cannot be fulfilled via classical networks. This paper presents a novel architecture of a noble software-defined wide area network (SD-WAN) that provides the ...

The current paper applies algorithms of machine learning to predict customer churn. The study employs 211,777 instances in the telecommunication sector with six attributes employed, e.g., data, length of stay, top-up, external communication, handset of ...