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RIONIDA: A novel algorithm for imbalanced data combining instance-based learning and rule induction

INFORMATION SCIENCES 2025年 708卷

作者： Gora, Grzegorz Skowron, Andrzej Univ Warsaw Stefana Banacha 2 PL-02097 Warsaw Poland PAS SYST RES INST NEWELSKA 6 PL-01447 Warsaw Poland

The article presents the Rule Induction with Optimal Neighbourhood for Imbalanced Data Algorithm (RIONIDA) learning algorithm based on combination of two widely-used empirical approaches: rule induction and instance-based learning for imbalanced data classification. The algorithm is a substantial extension of the well-known the Rule Induction with Optimal Neighbourhood Algorithm (RIONA) learning algorithm developed for balanced data. RIONIDA uses rules more general than in RIONA and realises a few additional concepts in comparison to RIONA, i.e. optimisation of the explicitly given performance measure defined over the confusion matrix, optimisation of weights for two classes, the idea of scaled rules, optimisation of parameters related to two latter ideas. RIONIDA, with decisions explainable for the user, is relatively fast and significantly outperforms the state-of-the-art algorithms analysed in the article.

关键词： Imbalanced learning Classification Supervised learning instance-based learning k nearest neighbours Rule induction

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RIONIDA: A Novel Algorithm for Imbalanced Data Combining instance-based learning and Rule Induction

RIONIDA: A Novel Algorithm for Imbalanced Data Combining Ins...

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International Joint Conference on Rough Sets (IJCRS)

作者： Gora, Grzegorz Skowron, Andrzej Univ Warsaw Stefana Banacha 2 PL-02097 Warsaw Poland PAS Syst Res Inst Newelska 6 PL-01447 Warsaw Poland

ISBN: (纸本)9783031656644;9783031656651

The article presents the RIONIDA learning algorithm based on combination of two widely-used empirical approaches: rule induction and instance-based learning for imbalanced data classification. The algorithm is a substantial extension of the well-known RIONA algorithm developed for balanced data. RIONIDA is relatively fast and significantly outperforms the state-of-the-art algorithms analysed in the paper.

关键词： Imbalanced learning Classification Supervised learning instance-based learning k Nearest Neighbours Rule Induction

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Multi-human Intelligence in instance-based learning 29th

Multi-human Intelligence in Instance-Based Learning

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29th International Conference on Neural Information Processing

作者： Gupta, Aadhar Uttrani, Shashank Paul, Gunjan Kariekar, Bhavik Dutt, Varun Indian Inst Technol Mandi Appl Cognit Sci Lab Kamand 175005 Himachal Prades India

ISBN: (纸本)9789819916412;9789819916429

Achieving decision-making that resembles humans is still a challenge for artificial intelligence (AI). Although researchers have successfully used techniques like deep reinforcement learning (DRL) and imitation learning (IL) to develop intelligent behavior in agents, however, such machine-learning-based methods may not resemble human choices. This study addresses this limitation by evaluating how a cognitive model based upon instance-based learning (IBL) theory matches human behavior on a simulation-based search-and-retrieval task. First, the simulation environment was developed using the Unity3D game engine. Next, four human players were recruited to play the simulation to generate human data. This data was then used to initialize the IBL models. In this research, we attempted to improve the quality of human data by sampling portions from the behavior data of multiple humans while maintaining the data size equivalent to the average size of each human's data. Results revealed that the models driven by the multi-human data doubled in the accuracy of matching the human choices. We also present a novel depiction of how the IBL model's decision-making improves with the variation in the number of human sources. Techniques where learning from human demonstrations is involved (e.g., IL) may benefit from these results by using multi-human data due to reduced noise and biases.

关键词： Human data Artificial intelligence instance-based learning Cognitive modeling Reinforcement learning Imitation learning

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THE HACKER IS INSIDE THE COMPANY! instance-based learning & Its Application to Defend Against Insider Hacking 25

THE HACKER IS INSIDE THE COMPANY! Instance-Based Learning & ...

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25th Annual Conference on Information Technology Education, SIGITE 2024

作者： Esquivel, David Trousdale, Shawn Khan, Mohammad Ariful Islam Aggarwal, Palvi Tosh, Deepak K. Canutillo Independent School District CanutilloTX United States Ysleta Independent School District El PasoTX United States The University of Texas at El Paso El PasoTX United States

ISBN: (纸本)9798400711060

This research was conducted by the authors while participating in the Cybersecurity Research Experience for Educators through Data Science (CREEDS), a Research Experience for Teachers (RET) summer program funded by the U.S. National Science Foundation at the University of Texas at El Paso (UTEP). The work explores the application of instance-based Modeling to understand the behavior of cyber attacker in order to ensure better defense. The study simulates the hacker choices among multiple targets with various reward/penalty structures. The fact that the common elements from human decision-making such as cognitive noise and memory decay influences the model has also been discussed. Ultimately, the authors propose strategies on how these techniques can be introduced in high school class rooms in order to make a bridge between advanced cybersecurity research and practical secondary math and computer science curricula. © 2024 Copyright held by the owner/author(s).

关键词： cognitive modeling cybersecurity high school education human factors instance-based learning

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SpeedyIBL: A comprehensive, precise, and fast implementation of instance-based learning theory

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BEHAVIOR RESEARCH METHODS 2023年第4期55卷 1734-1757页

作者： Nguyen, Thuy Ngoc Phan, Duy Nhat Gonzalez, Cleotilde Carnegie Mellon Univ Social & Decis Sci 5000 Forbes Ave Pittsburgh PA 15213 USA

instance-based learning theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks. Since it was first proposed almost two decades ago, multiple computational models have been constructed based on IBLT (i.e., IBL models). These models have been demonstrated to be very successful in explaining and predicting human decisions in multiple decision-making contexts. However, as IBLT has evolved, the initial description of the theory has become less precise, and it is unclear how its demonstration can be expanded to more complex, dynamic, and multi-agent environments. This paper presents an updated version of the current theoretical components of IBLT in a comprehensive and precise form. It also provides an advanced implementation of the full set of theoretical mechanisms, SpeedyIBL, to unlock the capabilities of IBLT to handle a diverse taxonomy of individual and multi-agent decision-making problems. SpeedyIBL addresses a practical computational issue in past implementations of IBL models, the curse of exponential growth, that emerges from memory-based tabular computations. When more observations accumulate over time, there is an exponential growth of the memory of instances that leads directly to an exponential slowdown of the computational time. Thus, SpeedyIBL leverages parallel computation with vectorization to speed up the execution time of IBL models. We evaluate the robustness of SpeedyIBL over an existing implementation of IBLT in decision games of increased complexity. The results not only demonstrate the applicability of IBLT through a wide range of decision-making tasks, but also highlight the improvement of SpeedyIBL over its prior implementation as the complexity of decision features the of agents increase. The library is open sourced for the use of the broad research community.

关键词： instance-based learning Cognitive models Decision from experience Python instance-based learning library

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Concepts of neighbors and their application to instance-based learning on relational data

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2024年 164卷

作者： Ayats, H. Ambre Cellier, Peggy Ferre, Sebastien Univ Rennes INSA Rennes CNRS InriaIRISA UMR 6074 F-35000 Rennes France

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances. We define a conceptual distance based on FCA concepts, and from there the notion of concepts of neighbors, which can be used as a basis for instance-based reasoning. Those definitions are given for both classical FCA and Graph-FCA. We provide efficient algorithms for computing concepts of neighbors, and we demonstrate their inference capabilities by presenting three different applications: query relaxation, knowledge graph completion, and relation extraction.

关键词： Relational data Knowledge graph instance-based learning Formal concept analysis Graph-FCA Concepts of neighbors

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Evaluation of instance-based learning and Q-learning Algorithms in Dynamic Environments

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IEEE ACCESS 2021年 9卷 138775-138790页

作者： Gupta, Anmol Roy, Partha Pratim Dutt, Varun Indian Inst Technol Roorkee PARIMAL Roorkee 247667 Uttar Pradesh India Indian Inst Technol Mandi Appl Cognit Sci Lab Kamand 175005 Himachal Prades India

Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment. Prior research has proposed cognitive (e.g., instance-based learning or IBL) and statistical (Q-learning) reinforcement learning algorithms. However, an evaluation of these algorithms in a single dynamic environment has not been explored. In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, "Frozen Lake," is used to train, generalize, and scale Q-learning and IBL algorithms. For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment. For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment. Results revealed that the IBL algorithm used less training time and generalized better to different environment variants. The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment. These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning. The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.

关键词： Heuristic algorithms Lakes Games Reinforcement learning Task analysis Unsupervised learning Prediction algorithms Reinforcement learning Q-learning instance-based learning openAI cognitive modeling frozen lake dynamic environment

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A New Hybrid instance-based learning Model for Decision-Making in the P2P Lending Market

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COMPUTATIONAL ECONOMICS 2021年第1期57卷 419-432页

作者： Babaei, Golnoosh Bamdad, Shahrooz Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Peer-to-Peer (P2P) lending has grown rapidly in the past years. Therefore, borrowers and lenders are provided with the opportunity of lending and borrowing independently of the banks. Lenders in the P2P lending market can share their total investment amount among different loans, so making a decision may be difficult for inexpert lenders. The aim of this study is to propose a novel decision-making framework in which instance-based learning as a lazy learning method and artificial neural networks as an eager learning approach are integrated. The proposed hybrid instance-based learning (HIBL) model has the ability to predict the return and risk of new loans and help investors to find the optimal portfolio. In order to check the effectiveness of our model, we use a real-world dataset from one of the most popular P2P lending marketplaces, namely Lending Club. Moreover, a comparison among our proposed model and a rating-based method reveals that the proposed HIBL model can improve investments in P2P lending.

关键词： P2P lending instance-based learning Artificial neural networks (ANNs) Investment decisions

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Using the instance-based learning Paradigm to Model Energy-Relevant Occupant Behaviors in Buildings

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COGNITIVE COMPUTATION 2020年第1期12卷 71-99页

作者： von Grabe, Joern Univ Liechtenstein Inst Architecture & Planning Furst Franz Josef Str FL-9490 Vaduz Liechtenstein

Human interactive behavior is accountable for most of the variance between the observed and predicted energy consumption of buildings, and is accordingly acknowledged as a major field of research into limiting building-related energy consumption. A thorough understanding of occupant behavior is critical to facilitate a more reliable prediction of energy consumption and identifying means by which pro-environmental behaviors can be promoted. Insights and models from psychology and sociology appear to be best suited to improving such understanding, and this article contributes to this end by developing and testing a cognitive model that serves as the core of a numerical human-building interaction model. The proposed implementation builds on instance-based learning, a well-established cognitive modeling paradigm, is integrated into a thermodynamic building model, and complemented by perception models for the approximation of the thermal and olfactory perception of the environment. The model successfully learns to interact plausibly with a set of elements of a model room-a heating system, a window, and the actor's clothing-in order to establish predefined room conditions. Accumulation of context-specific instances in the declarative memory, which are retrieved and blended in a decision situation, provide the model with the flexibility to adapt its actions to very different climatic contexts, represented by the locations Stuttgart, Madrid, Stockholm, and Melbourne. Moreover, the model manages to find appropriate compromises if need satisfaction requires contradictory actions, such as in situations where satisfaction of the olfactory need requires opening the window and satisfaction of the thermal need requires keeping it closed. Despite its obvious complexity, the model must be considered to be a basic model, which restricts the immediate comparability of its results to human behavior data. However, the successfully applied plausibility checks clearly indicate the value o

关键词： Cognitive modeling Energy-relevant behavior instance-based learning Prediction Social simulation

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An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

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TRAVEL BEHAVIOUR AND SOCIETY 2023年 33卷

作者： Gerzinic, Nejc Cats, Oded van Oort, Niels Hoogendoorn-Lanser, Sascha Bierlaire, Michel Hoogendoorn, Serge Delft Univ Technol Dept Transport & Planning Delft Netherlands Delft Univ Technol Mobil Innovat Ctr Delft Delft Netherlands Swiss Fed Inst Technol Lausanne Transport & Mobil Lab Lausanne Switzerland

Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference survey in August 2021, yielding 936 valid responses. The respondents were faced with static pre-trip information on the expected waiting time, followed by the actually experienced waiting time for their selected alternative. We analyse this data by means of an instance-based learning (IBL) approach to evaluate how individuals respond to service performance variation and how this impacts their future decisions. Different novel specifications of memory fading, captured by the IBL approach, are tested to uncover which describes the user behaviour best. Additionally, existing and new specification of inertia (habit) are tested. Our model outcomes reveal that the perception of unexpected waiting time is within the expected range of 2-3 times the value-of time. Travellers seem to place a higher reward on an early departure compared to a penalty for a late departure of equal magnitude. A cancelled service, after having made a booking, results in significant disutility for the passenger and a strong motivation to shift to a different provider. Considering memory decay, our results show that the most recent experience is by far the most relevant for the next decision, with memories fading quickly in importance. The role of inertia seems to gain importance with each additional consecutive choice for the same option, but then resetting back to zero following a shift in behaviour.

关键词： instance-based learning Memory decay Choice modelling Ride-hailing Waiting time Service reliability

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