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One-Class Classification for Selecting Synthetic Datasets in Meta-Learning

One-Class Classification for Selecting Synthetic Datasets in...

International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Parente, Regina R. Prudencio, Ricardo B. C. Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (纸本)9781728169262

algorithm selection is a challenging task in machine learning. Meta-learning treats algorithm selection as a supervised learning task, in which training examples (i.e., meta examples) are generated from experiments performed with a set of candidate algorithms in several datasets. The small availability of real datasets in some domains can make it difficult to generate good sets of meta-examples. An alternative is the use of synthetic datasets. Unfortunately, not all synthetic datasets can be considered equally relevant and representative compared to real datasets. Thus simply adopting a high number of arbitrary synthetic datasets increases the computational cost of performing experiments, without necessarily improving the quality of meta learning. In this paper, we treat the selection of relevant synthetic datasets for meta-learning as an One-Class Classification (OCC) problem. In this problem, it is assumed the availability of instances associated to a single class of interest (the positive class) and a large set of unlabelled instances (the unknown class). The objective is to classify which unlabelled instances most likely belong to the positive class. In our context, OCC techniques are used to select the most relevant synthetic datasets (unknown class), by considering the real datasets (positive class) available. In our work, we conducted experiments in a case study in which we adopted a data manipulation procedure to produce synthetic datasets and two OCC techniques for dataset selection. The results revealed that it was actually possible to select a reduced number of synthetic datasets while maintaining or even increasing meta-learning performance.

关键词： meta-learning algorithm selection one-class classification

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Revisiting where are the hard knapsack problems? via Instance Space Analysis

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COMPUTERS & OPERATIONS RESEARCH 2021年 128卷 105184-105184页

作者： Smith-Miles, Kate Christiansen, Jeffrey Munoz, Mario Andres Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

In 2005, David Pisinger asked the question "where are the hard knapsack problems?". Noting that the classical benchmark test instances were limited in difficulty due to their selected structure, he proposed a set of new test instances for the 0-1 knapsack problem with characteristics that made them more challenging for dynamic programming and branch-and-bound algorithms. This important work highlighted the influence of diversity in test instances to draw reliable conclusions about algorithm performance. In this paper, we revisit the question in light of recent methodological advances - in the form of Instance Space Analysis - enabling the strengths and weaknesses of algorithms to be visualised and assessed across the broadest possible space of test instances. We show where the hard instances lie, and objectively assess algorithm performance across the instance space to articulate the strengths and weaknesses of algorithms. Furthermore, we propose a method to fill the instance space with diverse and challenging new test instances with controllable properties to support greater insights into algorithm selection, and drive future algorithmic innovations. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： 0?1 Knapsack problem Instance Space Analysis Instance generation Instance difficulty Performance evaluation algorithm portfolios algorithm selection

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Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants

Integrated vs. Sequential Approaches for Selecting and Tunin...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Wang, Hao Doerr, Carola Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781450371285

When faced with a specific optimization problem, deciding which algorithm to apply is always a difficult task. Not only is there a vast variety of algorithms to select from, but these algorithms are often controlled by many hyperparameters, which need to be suitably tuned in order to achieve peak performance. Usually, the problem of selecting and configuring the optimization algorithm is addressed sequentially, by first selecting a suitable algorithm and then tuning it for the application at hand. Integrated approaches, commonly known as Combined algorithm selection and Hyperparameter (CASH) solvers, have shown promise in several applications. In this work we compare sequential and integrated approaches for selecting and tuning the best out of the 4,608 variants of the modular Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We show that the ranking of these variants depends to a large extent on the quality of the hyperparameters. Sequential approaches are therefore likely to recommend sub-optimal choices. Integrated approaches, in contrast, manage to provide competitive results at much smaller computational cost. We also highlight important differences in the search behavior of two CASH approaches, which build on racing (irace) and on model-based optimization (MIP-EGO), respectively.

关键词： algorithm selection algorithm Control Iterative Optimization Heuristics Black-Box Optimization

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A New Evaluation Method for Medical Image Information Hiding Techniques 42

A New Evaluation Method for Medical Image Information Hiding...

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42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Eze, Peter Parampalli, Udaya Evans, Robin Liu, Dongxi Univ Melbourne Comp & Informat Syst Melbourne Vic Australia CSIRO Data61 Distributed & Software Syst Sydney NSW Australia

ISBN: (纸本)9781728119908

Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.

关键词： medical image Information hiding algorithm selection security

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Online selection of CMA-ES Variants

Online Selection of CMA-ES Variants

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick van Rijn, Sander Back, Thomas Doerr, Carola Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ LIP6 Paris France CNRS Paris France

ISBN: (纸本)9781450361118

In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms. We build upon the theoretical work done by van Rijn et al. [PPSN'18], in which the potential of switching between different CMA-ES variants was quantified in the context of a modular CMA-ES framework. We demonstrate in this work that their proposed approach is not very reliable, in that implementing the suggested adaptive configurations does not yield the predicted performance gains. We propose a revised approach, which results in a more robust fit between predicted and actual performance. The adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested functions of the BBOB benchmark, with stable advantages of up to 23%. An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems. The module activation also suggests that additional gains are possible when including the (B)IPOP modules, which we have excluded for this present work.

关键词： Evolution Strategies algorithm Configuration algorithm selection

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MAPFAST: A Deep algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings 21

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Find...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Jingyao Ren Vikraman Sathiyanarayanan Eric Ewing Baskin Senbaslar Nora Ayanian University of Southern California

ISBN: (纸本)9781713832621

Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs.

关键词： Multi-Agent path finding algorithm selection Deep learning algorithms selectors Dataset Nondeterministic polynomial-time hard shortest path FINDINGS BRIEFCASE Byways analysis of algorithm

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Meta Learning Recommendation System for Classification

Meta Learning Recommendation System for Classification

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作者： Clarence O. WilliaIII Air Force Institute of Technology Air University Air Education and Training Command

学位级别：硕士

A data driven approach is an emerging paradigm for the handling of analytic prob- lems. In this paradigm the mantra is to let the data speak freely. However, when using machine learning algorithms, the data does not naturally reveal the best or even a good approach for algorithm choice. One method to let the algorithm reveal itself is through the use of Meta Learning, which uses the features of a dataset to determine a useful model to represent the entire dataset. This research proposes an improve- ment on the meta-model recommendation system by adding classification problems to the candidate problem space with appropriate evaluation metrics for these additional problems. This research predicts the relative performance of six machine learning algorithms using support vector regression with a radial basis function as the meta learner. Six sets of data of various complexity are explored using this recommendation system and at its best, the system recommends the best algorithm 67% of the time and a "good" algorithm from 67% to 100% of the time depending on how "good" is defined.

关键词： Meta Learning Meta Modeling Recommendation System algorithm selection

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Private selection from Private Candidates 2019

Private Selection from Private Candidates

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51st Annual ACM SIGACT Symposium on Theory of Computing (STOC)

作者： Liu, Jingcheng Talwar, Kunal Univ Calif Berkeley Berkeley CA 94720 USA Google Brain Mountain View CA 94043 USA

ISBN: (纸本)9781450367059

Differentially Private algorithms often need to select the best amongst many candidate options. Classical works on this selection problem require that the candidates' goodness, measured as a real-valued score function, does not change by much when one person's data changes. In many applications such as hyperparameter optimization, this stability assumption is much too strong. In this work, we consider the selection problem under a much weaker stability assumption on the candidates, namely that the score functions are differentially private. Under this assumption, we present algorithms that are near-optimal along the three relevant dimensions: privacy, utility and computational efficiency. Our result can be seen as a generalization of the exponential mechanism and its existing generalizations. We also develop an online version of our algorithm, that can be seen as a generalization of the sparse vector technique to this weaker stability assumption. We show how our results imply better algorithms for hyperparameter selection in differentially private machine learning, as well as for adaptive data analysis.

关键词： Differential privacy Hyperparameter tuning algorithm selection Adaptive data analysis

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The ELAPS framework: Experimental Linear Algebra Performance Studies

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2019年第2期33卷 353-365页

作者： Peise, Elmar Bientinesi, Paolo Rhein Westfal TH Aachen AICES Schinkelstr 2 D-52062 Aachen Germany

In scientific computing, optimal use of computing resources comes at the cost of extensive coding, tuning, and benchmarking. While the classic approach of "features first, performance later" is supported by a variety of tools such as Tau, Vampir, and Scalasca, the emerging performance-centric approach, in which both features and performance are primary objectives, is still lacking suitable development tools. For dense linear algebra applications, we fill this gap with the Experimental Linear Algebra Performance Studies (ELAPS) framework, a multi-platform open-source environment for easy, fast, and yet powerful performance experimentation and prototyping. In contrast to many existing tools, ELAPS targets the beginning of the development process, assisting application developers in both algorithmic and optimization decisions. With ELAPS, users construct experiments to investigate how performance and efficiency depend on factors such as caching, algorithmic parameters, problem size, and parallelism. Experiments are designed either through Python scripts or a specialized Graphical User Interface (GUI), and run on a spectrum of architectures, ranging from laptops to accelerators and clusters. The resulting reports provide various metrics and statistics that can be analyzed both numerically and visually. In this article, we introduce ELAPS and illustrate its practical value in guiding critical performance decisions already in early development stages.

关键词： Performance dense linear algebra experimentation algorithm selection performance prediction

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A sequential algorithm portfolio approach for black box optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 559-570页

作者： He, Yaodong Yuen, Shiu Yin Lou, Yang Zhang, Xin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

关键词： algorithm portfolio algorithm selection Heuristic algorithms Optimization problems Performance prediction

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