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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Failure-Driven Transformational Case Reuse of Explanation Strategies in CloodCBR 31st

Failure-Driven Transformational Case Reuse of Explanation St...

引用

31st International Conference on Case-Based Reasoning Research and Development (ICCBR)

作者： Nkisi-Orji, Ikechukwu Palihawadana, Chamath Wiratunga, Nirmalie Wijekoon, Anjana Corsar, David Robert Gordon Univ Sch Comp Aberdeen Scotland

ISBN: (纸本)9783031401763;9783031401770

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves transforming suboptimal solutions using relevant components from nearest neighbours in sparse case bases. To represent these explanation strategies, we use behaviour trees and demonstrate their usefulness in solving similar problems. Our approach uses failures as a starting point for generating new solutions, analysing the causes and contributing factors to the failure. From this analysis, new solutions are generated through a nearest neighbour-based transformation of previous solutions, resulting in solutions that address the failure. We compare different approaches for reusing solutions of the nearest neighbours and empirically evaluate whether the transformed solutions meet the required explanation intents. Our proposed approach has the potential to significantly improve problem-solving efficiency in sparse case bases with complex case solutions.

关键词： case-based reasoning case reuse explainable AI behaviour trees

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Robust Classification of High-Dimensional Data Using Data-Adaptive Energy Distance

Robust Classification of High-Dimensional Data Using Data-Ad...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Choudhury, Jyotishka Ray Saha, Aytijhya Roy, Sarbojit Dutta, Subhajit Indian Stat Inst Kolkata India King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Indian Inst Technol Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9783031434235;9783031434242

Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These classifiers are free of tuning parameters and are robust, in the sense that they are devoid of any moment conditions of the underlying data distributions. It is shown that they yield perfect classification in the HDLSS asymptotic regime, under some fairly general conditions. The comparative performance of the proposed classifiers is also investigated. Our theoretical results are supported by extensive simulation studies and real data analysis, which demonstrate promising advantages of the proposed classification techniques over several widely recognized methods.

关键词： Classification Data Mining Generalized Energy High-Dimensional Asymptotics

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Kryszkiewicz's Relation for Indiscernibility of Objects in Data Tables Containing Missing Values

Kryszkiewicz's Relation for Indiscernibility of Objects in D...

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International Joint Conference on Rough Sets (IJCRS)

作者： Nakata, Michinori Saito, Norio Sakai, Hiroshi Fujiwara, Takeshi Josai Int Univ Fac Management & Informat Sci 1 Gumyo Togane Chiba 2838555 Japan Kyushu Inst Technol Dept Math & Comp Aided Sci Fac Engn Tobata Kitakyushu 8048550 Japan Tokyo Univ Informat Sci Fac Informat 4-1 OnaridaiWakaba Ku Chiba 2658501 Japan

ISBN: (纸本)9783031509582;9783031509599

We check Kryszkiewicz's approach for rough sets in data tables containing missing values in terms of Lipski's approach based on possible world semantics. The relation for indiscernibility of objects used by Kryszkiewicz, which most authors use, derives a pair of lower and upper approximations as the actual approximations to a target set. This means that Kryszkiewicz's approach is accompanied by information loss because what Lipski's approach derives is the lower and upper bounds of the actual approximations. It is clarified that Kryszkiewicz's relation for indiscernibility is equal to the union of possible indiscernibility relations in Lipski's approach. As a result, the lower and the upper approximation derived from Kryszkiewicz's relation are equal to the lower bound of the actual lower approximation and the upper bound of the actual upper approximation, respectively. Bridging the gap between the two approaches, we propose another relation for indiscernibility that is equal to the intersection of possible indiscernibility relations. By using Kryszkiewicz's relation and the proposed relation, Kryszkiewicz's approach can derive the same approximations as Lipski's one. Therefore, we can keep using Kryszkiewicz's approach without information loss.

关键词： rough sets incomplete information missing values lower and upper approximations possible world semantics

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A Baseline Generative Probabilistic Model for Weakly Supervised Learning 1

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Papadopoulos, Georgios Silavong, Fran Moran, Sean JPMorgan Chase & Co 25 Bank St London E14 5JP England

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners. Furthermore, to address ambitious real-world use-cases there is usually the requirement that the data come labelled with high-quality annotations that can facilitate the training of a supervised model. Manually labelling data with high-quality labels is generally a time-consuming and challenging task and often this turns out to be the bottleneck in a machine learning project. Weakly Supervised Learning (WSL) approaches have been developed to alleviate the annotation burden by offering an automatic way of assigning approximate labels (pseudo-labels) to unlabelled data based on heuristics, distant supervision and knowledge bases. We apply probabilistic generative latent variable models (PLVMs), trained on heuristic labelling representations of the original dataset, as an accurate, fast and cost-effective way to generate pseudo-labels. We show that the PLVMs achieve state-of-the-art performance across four datasets. For example, they achieve 22% points higher F1 score than Snorkel in the class-imbalanced Spouse dataset. PLVMs are plug-and-playable and are a drop-in replacement to existing WSL frameworks (e.g. Snorkel) or they can be used as baseline high-performance models for more complicated algorithms, giving practitioners a compelling accuracy boost.

关键词： Weakly Supervised Learning Generative Models Probabilistic Models

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Isabelle Formalisation of Original Representation Theorems 16th

Isabelle Formalisation of Original Representation Theorems

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16th International Conference on Intelligent Computer Mathematics (CICM)

作者： Caminati, Marco B. Lancaster Univ Leipzig Sch Computingm & Commun Nikolaistr 10 D-04109 Leipzig Germany

ISBN: (纸本)9783031427527;9783031427534

In a recent paper, new theorems linking apparently unrelated mathematical objects (event structures from concurrency theory and full graphs arising in computational biology) were discovered by cross-site data mining on huge databases, and building on existing Isabelle-verified event structures enumeration algorithms. Given the origin and newness of such theorems, their formal verification is particularly desirable. This paper presents such a verification via Isabelle/HOL definitions and theorems, and exposes the technical challenges found in the process. The introduced formalisation completes the verification of Isabelle-verified event structure enumeration algorithms into a fully verified framework to link event structures to full graphs.

关键词： Formal verification

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Monte Carlo Tree Search with Metaheuristics 22nd

Monte Carlo Tree Search with Metaheuristics

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Mandziuk, Jacek Walczak, Patryk Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9783031425073;9783031425080

Monte Carlo Tree Search/Upper Confidence bounds applied to Trees (MCTS/UCT) is a popular and powerful search technique applicable to many domains, most frequently to searching game trees. Even though the algorithm has been widely researched, there is still room for its improvement, especially when combined with metaheuristics or machine learning methods. In this paper, we revise and experimentally evaluate the idea of enhancing MCTS/UCT with game-specific heuristics that guide the playout (simulation) phase. MCTS/UCT with the proposed guiding mechanism is tested on two popular board games: Othello and Hex. The enhanced method clearly defeats the well-known Alpha-beta pruning algorithm in both games, and for the more complex game (Othello) is highly competitive to the vanilla MCTS/UCT formulation.

关键词： Monte Carlo Tree Search Upper Confidence bounds applied to Trees Two-player games Metaheuristics

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RLMixer: A Reinforcement Learning Approach for Integrated Ranking with Contrastive User Preference Modeling 27th

RLMixer: A Reinforcement Learning Approach for Integrated Ra...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Jing Zhao, Mengchen Xia, Wei Dong, Zhenhua Tang, Ruiming Zhang, Rui Hao, Jianye Chen, Guangyong Heng, Pheng-Ann Chinese Univ Hong Kong Ma Liu Shui Hong Kong Peoples R China Huawei Noahs Ark Lab Quebec City PQ Canada Tsinghua Univ Beijing Peoples R China Tianjin Univ Tianjin Peoples R China Zhejiang Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783031333798;9783031333804

There is a strong need for industrial recommender systems to output an integrated ranking of items from different categories, such as video and news, to maximize overall user satisfaction. Integrated ranking faces two critical challenges. First, there is no universal metric to evaluate the contribution of each item due to the huge discrepancies between items. Second, user's short-term preference may shift fast between diverse items during her interaction with the recommender system. To address the above challenges, we propose a reinforcement learning (RL) based framework called RLMixer to approach the sequential integrated ranking problem. Benefiting from the credit assignment mechanism, RLMixer can decompose the overall user satisfaction to items of different categories, so that they are comparable. To capture the user's short-term preference, RLMixer explicitly learns user interest vectors by a carefully designed contrastive loss. In addition, RLMixer is trained in a fully offline manner for the convenience in industrial applications. We show that RLMixer significantly outperforms various baselines on both public PRM datasets and industrial datasets collected from a widely used AppStore. We also conduct online A/B tests on millions of users through the AppStore. The results show that RLMixer brings over 4% significant revenue gain.

关键词： Integrated ranking Reinforcement Learning Contrastive Learning

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Cannabis Use Estimators Within Canadian Population Using Social Media Based on Deep Learning Tools 22nd

Cannabis Use Estimators Within Canadian Population Using Soc...

引用

22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Ibrahim, Doaa Inkpen, Diana Al Osman, Hussein Ottawa Univ Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9783031425073;9783031425080

Cannabis has become the most used drug worldwide with the highest risks and associated criminal problems in many countries. Therefore, identifying users at risk of cannabis use is an important and essential task. This will help doctors and people with authority to react fast and on time. We used two datasets (8,725 users in total) to build a strong model for predicting users at-risk in a sample of Twitter users that is representative of the Canadian population. Using a Bidirectional GRU classifier, we achieved high accuracy and F1-score (84.74% and 87.56%, respectively). This model was applied on the population dataset to predict the patterns of cannabis users all over Canada. The geographic patterns of cannabis users highly match the cannabis users statistics reported by Statistics Canada (with a small overall difference of 1.4%). To the best of our knowledge, this is the first study to demonstrate that the number of cannabis users in a country can be accurately estimated by analyzing a representative sample of Twitter users from various regions of the country.

关键词： CNN LSTM BERT BioBERT attention cannabis marijuana weed substance use classification risk behavior natural language processing social media transfer learning deep learning

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Hybrid SkipAwareRec: A Streaming Music Recommendation System 22nd

Hybrid SkipAwareRec: A Streaming Music Recommendation System

引用

22nd EPIA Conference on artificial intelligence (EPIA)

作者： Ramos, Rui Oliveira, Lino Vinagre, Joao INESC TEC Porto Portugal European Commiss Joint Res Ctr Seville Spain Univ Porto Fac Sci Porto Portugal

ISBN: (纸本)9783031490071;9783031490088

In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.

关键词： Sequential recommendation Next best action recommendation Implicit negative feedback Reinforcement learning

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Covariance Kernel Learning Schemes for Gaussian Process Based Prediction Using Markov Chain Monte Carlo 22nd

Covariance Kernel Learning Schemes for Gaussian Process Base...

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Roy, Gargi Warrior, Kane Chakrabarty, Dalia Brunel Univ London Kingston Lane London UB8 3PH Uxbridge England

ISBN: (纸本)9783031490071;9783031490088

Probabilistic supervised learning within the Bayesian paradigm typically use Gaussian Processes (GPs) to model the sought function, and provide a means for securing reliable uncertainties in said functional learning, while offering interpretability. Prediction of the output of such a learnt function is closed-form in this approach. In this work, we present GP based learning of the functional relation between two variables, using various kinds of kernels that are called in to parametrise the covariance function of the invoked GP. However, such covariance kernels are typically parametric in the literature, with hyperparameters that are learnt from the data. Here, we discuss a new nonparametric covariance kernel, and compare its performance against existing non-stationary and stationary kernels, as well as against Deep Neural Networks. We present results on both univariate and multivariate data, to demonstrate the range of applicability of the presented learning scheme.

关键词： Covariance kernel hyperparameter Stationary Nonparametric Markov Chain Monte Carlo Gaussian process

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