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Supervised learning in the presence of concept drift a modelling framework

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arXiv 2020年

作者： Straat, Michiel Abadi, Fthi Kan, Zhuoyun Göpfert, Christina Hammer, Barbara Biehl, Michael Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands Aksum University Institute of Engineering and Technology Computing Science Department Axum Tigray Ethiopia Bielefeld University CITEC Machine Learning Group Bielefeld33594 Germany

We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based learning Vector Quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units (ReLU). Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions

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arXiv 2023年

作者： Kilaj, Ardita Käser, Silvan Wang, Jia Straňák, Patrik Schwilk, Max Xu, Lei von Lilienfeld, O. Anatole Küpper, Jochen Meuwly, Markus Willitsch, Stefan Department of Chemistry University of Basel Klingelbergstrasse 80 Basel4056 Switzerland Center for Free-Electron Laser Science CFEL Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoONM5S 3H6 Canada Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data BIFOLD Germany Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Department of Chemistry Universität Hamburg Martin-Luther-King-Platz 6 Hamburg20146 Germany Center for Ultrafast Imaging Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Department of Chemistry Brown University ProvidenceRI02912 United States

Recent advances in experimental methodology enabled studies of the quantum-state- and conformational dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled Ca+ ions in an ion trap. The rate coefficients for the total reaction were found to strongly depend on both the conformation of DBB and the electronic state of Ca+. In the (4p) 2P1/2 and (3d) 2D3/2 excited states of Ca+, the reaction is capture-limited and faster for the gauche conformer due to long-range ion-dipole interactions. In the (4s) 2S1/2 ground state of Ca+, the reaction rate for s-trans DBB still conforms with the capture limit, while that for gauche DBB is strongly suppressed. The experimental observations were analysed with the help of adiabatic capture theory, ab-initio calculations and reactive molecular dynamics simulations on a machine-learned full-dimensional potential energy surface of the system. The theory yields near-quantitative agreement for s-trans-DBB, but overestimates the reactivity of the gauche- conformer compared to the experiment. The present study points to the important role of molecular geometry even in strongly reactive exothermic systems and illustrates striking differences in the reactivity of individual conformers in gas-phase ion-molecule reactions. © 2023, CC BY-SA.

关键词： Electronic states

FXAM: A Unified and Fast Interpretable Model for Predictive Analytics

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arXiv 2021年

作者： Jiang, Yuanyuan Ding, Rui Qiao, Tianchi Zhu, Yunan Han, Shi Zhang, Dongmei School of Statistics Renmin University of China Haidian District Beijing100872 China Microsoft Research Asia Haidian District Beijing100080 China School of Computer Science and Engineering Southeast University Jiangsu Province Nanjing211189 China School of Information Science and Technology University of Science and Technology of China Anhui Province He Fei230031 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM’s modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM’s training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM’s inherent interpretability for predictive analytics. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

Antisymmetry rules of response properties in certain chemical spaces

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arXiv 2025年

作者： Shiraogawa, Takafumi Krug, Simon León Ehara, Masahiro von Lilienfeld, O. Anatole Institute for Molecular Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Research Center for Computational Science National Institutes of Natural Sciences 38 Nishigonaka Myodaiji Okazaki444-8585 Japan The Graduate University for Advanced Studies 38 Nishigonaka Myodaiji Okazaki444-8585 Japan Machine Learning Group Technische Universität Berlin Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoM5S3H6 Ontario Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoM5S 3E4 Ontario Canada Vector Institute for Artificial Intelligence TorontoM5S 1M1 Ontario Canada Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Physics University of Toronto St. George Campus TorontoM5S 1A7 Ontario Canada Acceleration Consortium University of Toronto TorontoM5R 0A3 Ontario Canada

Understanding chemical compound space (CCS), a set of molecules and materials, is crucial for the rational discovery of molecules and materials. Concepts of symmetry have recently been introduced into CCS to account for near degeneracies and differences in electronic energies between iso-electronic materials. In this work, we present approximate relationships of response properties based on a first-principles view of CCS. They have been derived from perturbation theory and antisymmetry considerations involving nuclear charges. These rules allow approximate predictions of relative response properties of pairs of distinct compounds with opposite nuclear charge variations from a highly symmetric reference material, without the need for experiments or quantum chemical calculations of each compound. We numerically and statistically verified these rules for electric and magnetic response properties (electric dipole moment, polarizabilities, hyperpolarizabilities, and magnetizabilities) among charge-neutral and iso-electronic BN-doped polycyclic aromatic hydrocarbon derivatives of naphthalene, anthracene, and pyrene. Our analysis indicates that, despite their simplicity, antisymmetry rule-based predictions are remarkably accurate, enabling dimensionality reduction of CCS. Response properties in alchemical perturbation density functional theory were investigated to clarify the origin of this predictive power. © 2025, CC BY.

关键词： Naphthalene

Multi-label Classification with High-rank and High-order Label Correlations

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

Arabic handwritten character recognition based on convolution neural networks and support vector machine

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arXiv 2020年

作者： Shams El Den, Mahmoud Yassien Elsonbaty, Amira A. ElSawy, Wael Z. Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Electronic and Communication Higher Institute of Engineering and Technology New Damietta Egypt Department of Business Information System Faculty of Management Technology and Information System PortSaid University 42511 Egypt

Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

Enhancing fruit disease classification with an advanced 3D shallow deep neural network for precise and efficient identification

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Expert Systems with Applications 2025年

作者： Hridaynath Pandurang Khandagale Sangram Tukaram Patil Vikram Sampat Gavali Amrita Arvind Manjrekar Pratap Pandurang Halkarnikar Research Scholar Department of Technology D Y Patil Agricultural and Technical University Talsande Kolhapur Maharashtra India Department of Technology Shivaji University Kolhapur Maharashtra India School of Engineering & Technology D Y Patil Agriculture & Technical University Warana – Wathar Road Talsande Fata Talsande Kolhapur India Department of Artificial Intelligence & Machine Learning New Institute of Technology Kolhapur Maharashtra India Computer Science and Technology Department of Technology Shivaji University Kolhapur Maharashatra India Dept of Computer Engineering Dr. D.Y.Patil Institute of Engineering Management & Research Akurdi Pune India

Diseases in fruits cause major problems in the agricultural industry and lead to significant economic loss. These diseases reduce yields, degrade the variety and can lead to crops being withdrawn from cultivation. Existing methods rely on heavy-weight architectures, which require higher storage capacity and expensive training operations due to the large number of parameters involved. In this paper, Enhancing Fruit Disease Classification with an Advanced 3D Shallow Deep Neural Network for Precise and Efficient Identification (EFDDNN-EI) is proposed. Here, the input images are taken from two datasets: (i) Strawberry Diseases dataset, (ii) Apple Fruit Disease dataset. The input images from both datasets are pre-processed by Regularized Bias-Aware Ensemble Kalman Filtering (RBAEKF) to eliminate unwanted noise. The pre-processed images are fed to the Newton Time-Extracting Wavelet Transform (NTEWT) to extract relevant attributes, such as color, texture, shape, size. The extracted features are fed into the 3D Shallow Deep Neural Network (3DSDNN). The 3DSDNN classifies the input images from the Strawberry Diseases dataset as botrytis cinerea, slugs, aphids, sunburn, thrips, root rot, vegetable green insects, viral diseases, powdery mildew and healthy leaves. Similarly, images from the Apple Fruit Disease dataset are classified into categories, like Blotch Apple, Normal Apple, Rot Apple, Scab Apple. Finally, Crisscross Harris Hawks Optimizer (CCHHO) is employed to enhance EFDDNN-EI that can classify the fruit diseases by maximizing the accuracy and minimizing the computation time. The proposed EFDDNN-EI approach is implemented, and the performance metrics, like recall, f1-score, precision, testing accuracy, testing loss and ROC to assess the efficacy of the method. The EFDDNN-EI technique achieves 16.89%, 18.57% and 27.68% higher testing accuracy, and 18.81%, 26.53% and 30.62% higher recall compared to the existing approaches: Strawberry Disease Detection under Transfer Lea

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

Voluntary safety commitments provide an escape from over-regulation in AI development

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arXiv 2021年

作者： Han, The Anh Lenaerts, Tom Santos, Francisco C. Pereira, Luís Moniz School of Computing Engineering and Digital Technologies Teesside University MiddlesbroughTS1 3BA United Kingdom Machine Learning Group Université Libre de Bruxelles Boulevard du Triomphe CP212 Brussels1050 Belgium Artificial Intelligence Lab Vrije Universiteit Brussel Boulevard de la Plaine 2 Brussels1050 Belgium INESCID and Instituto Superior Tecnico Universidade de Lisboa Portugal Universidade Nova de Lisboa Caparica2829-516 Portugal

With the introduction of artificial intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as the hidden biases in their creation have led to a demand for regulation to address such issues. Yet blindly regulating an innovation process that is not well understood, may stifle this process and reduce benefits that society may gain from the generated technology, even under the best intentions. In this paper, starting from a baseline model that captures the fundamental dynamics of a race for domain supremacy using AI technology, we demonstrate how socially unwanted outcomes may be produced when sanctioning is applied unconditionally to risk-taking, i.e. potentially unsafe, behaviours. As an alternative to resolve the detrimental effect of over-regulation, we propose a voluntary commitment approach wherein technologists have the freedom of choice between independently pursuing their course of actions or establishing binding agreements to act safely, with sanctioning of those that do not abide to what they pledged. Overall, this work reveals for the first time how voluntary commitments, with sanctions either by peers or an institution, leads to socially beneficial outcomes in all scenarios envisageable in a short-term race towards domain supremacy through AI technology. These results are directly relevant for the design of governance and regulatory policies that aim to ensure an ethical and responsible AI technology development process. © 2021, CC BY.

关键词： Game theory