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IEEE International Conference on Image Processing

作者： Shu Zhang Qi Zhu Amit Roy-Chowdhury SONY Electronics Inc. University of California

ISBN: (纸本)9781467399623

Computer vision algorithms are known to be extremely sensitive to the environmental conditions in which the data is captured, e.g., lighting conditions and target density. Tuning of parameters or choosing a completely new algorithm is often needed to achieve a certain performance level. In this paper, we focus on this problem and propose a framework to automatically choose the "best" algorithm-parameter combination (often referred to as the best algorithm for simplicity in this paper) for a certain input data. This necessitates developing a mechanism to switch among different algorithms and parameters as the nature of the input video changes. Specifically, our proposed algorithm calculates a similarity function between a test video segment and a training video segment. Similarity between training and test dataset indicates the same algorithm can be applied to both of them. We design a cost function with this similarity measure and a constraint on the number of switches. In the experiments, we apply our algorithm to the problem of pedestrian detection. We show how to adaptively select among 7 algorithm-parameter combinations and provide promising results on 3 publicly available datasets.

关键词： algorithm selection Adaptation Pedestrian detection algorithms Level of Performance similar measure Switches Cost functions images computer vision Choose Dataset indium

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An algorithm selection based platform for image understanding using high-level symbolic feedback and machine learning

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2015年第3期6卷 417-434页

作者： Lukac, Martin Kameyama, Michitaka Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 980 Japan

Natural image processing and understanding encompasses hundreds of different algorithms. Each algorithm generates best results for a particular set of input features and configurations of the objects/regions in the input image (environment). To obtain the best possible result of processing in a reliable manner, we propose an algorithm selection approach that selects the best algorithm for a each input image. The proposed algorithm selection starts by first selecting an algorithm using low level features such as color intensity, histograms, spectral coefficients or so and a user given context if available. The resulting high-level image description is analyzed for logical inconsistencies (contradictions) and image regions that must be processed using a different algorithm are selected. The high-level description and the optional user-given context are used by a Bayesian Network to estimate the cause of the error in the processing. The same Bayesian Network also generates new candidate algorithm for each region containing the contradiction in an iterative manner. This iterative selection stops when the high-level inconsistencies are all resolved or no more different algorithms can be selected. We also show that when inconsistencies can be detected, our framework is able to improve high-level description when compared with single algorithms. In order for such complex and iterative processing being computationally tractable we also introduce a hardware platform based on reconfigurable VLSI that is well suited as the platform of the proposed approach. We show that the algorithm selected approach is ideally suited for either a hybrid type VLSI processor or for a Logic-In-Memory processing platform.

关键词： Image processing Contour extraction algorithm selection Bayesian networks

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Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods

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INFORMATION SCIENCES 2015年 301卷 181-194页

作者： Ferrari, Daniel Gomes de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Nat Comp Lab LCoN Sao Paulo Brazil

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the algorithm selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning;that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects;and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Clustering Problem characterization algorithm ranking algorithm selection Meta-knowledge Meta-learning systems

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Fast algorithm selection Using Learning Curves 14th

Fast Algorithm Selection Using Learning Curves

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14th International Symposium on Intelligent Data Analysis (IDA)

作者： van Rijn, Jan N. Abdulrahman, Salisu Mamman Brazdil, Pavel Vanschoren, Joaquin Leiden Univ Leiden Netherlands Univ Porto Oporto Portugal Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783319244655

One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many solutions have been proposed that attempt to predict which classifiers are most promising to try. As the first recommended classifier is not always the correct choice, multiple recommendations should be made, making this a ranking problem rather than a classification problem. Even though this is a well studied problem, there is currently no good way of evaluating such rankings. We advocate the use of Loss Time Curves, as used in the optimization literature. These visualize the amount of budget (time) needed to converge to a acceptable solution. We also investigate a method that utilizes the measured performances of classifiers on small samples of data to make such recommendation, and adapt it so that it works well in Loss Time space. Experimental results show that this method converges extremely fast to an acceptable solution.

关键词： algorithm selection Meta-learning Subsampling

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From Sequential algorithm selection to Parallel Portfolio selection 1

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9th International Conference on Learning and Intelligent OptimizatioN (LION)

作者： Lindauer, M. Hoos, Holger H. Hutter, F. Univ Freiburg D-79106 Freiburg Germany Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (数字)9783319190846

ISBN: (纸本)9783319190846;9783319190839

In view of the increasing importance of hardware parallelism, a natural extension of per-instance algorithm selection is to select a set of algorithms to be run in parallel on a given problem instance, based on features of that instance. Here, we explore how existing algorithm selection techniques can be effectively parallelized. To this end, we leverage the machine learning models used by existing sequential algorithm selectors, such as 3S, ISAC, SATzilla and ME-ASP, and modify their selection procedures to produce a ranking of the given candidate algorithms;we then select the top n algorithms under this ranking to be run in parallel on n processing units. Furthermore, we adapt the pre-solving schedules obtained by aspeed to be effective in a parallel setting with different time budgets for each processing unit. Our empirical results demonstrate that, using 4 processing units, the best of our methods achieves a 12-fold average speedup over the best single solver on a broad set of challenging scenarios from the algorithm selection library.

关键词： algorithm selection Parallel portfolios Constraint solving Answer Set Programming

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Evaluation of algorithm selection Approach for Semantic Segmentation Based on High-Level Information Feedback

Evaluation of Algorithm Selection Approach for Semantic Segm...

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International Conference on Information and Digital Technologies (IDT)

作者： Lukac, Martin Abdiyeva, Kamila Kameyama, Michitaka Nazarbayev Univ Dept Comp Sci Sch Sci & Technol Astana Kazakhstan Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan

ISBN: (纸本)9781467371865

In this paper we discuss certain theoretical properties of the algorithm selection approach to the problem of semantic segmentation in computer vision. We show that an algorithm's score depends on final task. Thus to properly evaluate an algorithm and to determine its suitability, precise score value obtained on well formulated tasks can be used only. When an algorithm suitability is well known, the algorithm can be efficiently used for a task by applying it in the most favorable environmental conditions determined during the evaluation. However, high quality algorithm selection is possible only if each algorithm suitability is well known because only then the algorithm selection result can improve the best possible result given by a single algorithm. The task dependent evaluation is demonstrated on segmentation and object recognition. Additionally, we also discuss the importance of high level symbolic knowledge in the selection process. The importance of this symbolic hypothesis is demonstrated on a set of learning experiments with both a Bayesian Network and SVM. We show that task dependent evaluation is required to allow efficient algorithm selection. Also by studying symbolic preference of algorithms for semantic segmentation we show that algorithm selection accuracy can be improved by 10 to 15%.

关键词： algorithm selection Semantic Segmentation Machine Learning

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Feature selection for SUNNY: a Study on the algorithm selection Library 27

Feature Selection for SUNNY: a Study on the Algorithm Select...

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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Amadini, Roberto Biselli, Fabio Gabbrielli, Maurizio Liu, Tong Mauro, Jacopo Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy INRIA FOCUS Team Paris France Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781509001637

Given a collection of algorithms, the algorithm selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of the AS library (ASlib). Results indicate that a handful of features is enough to reach similar, if not better, performance of the original SUNNY approach that uses all the available features. We also present sunny-as: a tool for using SUNNY on a generic ASlib scenario.

关键词： algorithm Portfolio algorithm selection Feature selection Machine Learning

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Automated model selection for multivariate anomaly detection in manufacturing systems

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年 1-19页

作者： Engbers, Hendrik Freitag, Michael Univ Bremen BIBA Bremer Inst Prod & Logistik GmbH Hochschulring 20 D-28359 Bremen Germany Univ Bremen Fac Prod Engn Badgasteiner Str 1 D-28359 Bremen Germany

As machine learning is widely applied to improve the efficiency and effectiveness of manufacturing systems, the automated selection of appropriate algorithms and hyperparameters becomes increasingly important. This paper presents a model selection approach to multivariate anomaly detection for applications in manufacturing systems using a multi-output regression-based meta-learning method. The proposed method exploits the capabilities of meta-learning to explore and learn the intricate relationships within multivariate data sets in order to select the best anomaly detection model. It also facilitates the construction of an ensemble of algorithms with dynamically assigned weights based on their respective performance levels. In addition to the framework, new meta-features for the application domain are presented and evaluated. Experiments show the proposed method can be successfully applied to achieve significantly better results than benchmark approaches. This enables an automated selection of algorithms that can be used for enhanced anomaly detection under changing operating conditions.

关键词： Meta-learning algorithm selection Anomaly detection Multivariate manufacturing data

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Sentiment Analysis as a Text Categorization Task: A Study on Feature and algorithm selection for Italian Language

Sentiment Analysis as a Text Categorization Task: A Study on...

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IEEE International Conference on Data Science and Advanced Analytics

作者： Stefano Ferilli Berardina De Carolis Floriana Esposito Domenico Redavid Dipartimento di Informatica Università degli Studi di Bari Bari Italy Artificial Brain S.r.l. Bari Italy

ISBN: (纸本)9781467382748

The availability on the Internet of huge amounts of blog posts, messages and comments allows to study the attitude of people on various topics. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at studying, analyzing and classifying text documents based on the underlying opinions expressed by their authors on various topics. While this is a tough task, because it is related to psychological aspects that are not always immediately evident in the lexical and syntactical aspects of the sentences, its importance may be paramount for several applications such as market analysis, political polls, etc. Fundamental pre-processing techniques for this task come from the area of Natural Language Processing, which may pose additional problems when the language of interest is different than English, and thus less (or less reliable) resources are available to extract the needed data from the text. This paper studies the performance of Sentiment Analysis, seen as a Text Categorization task, depending on the use of different classifiers and different features. While the approach is general, we focus on texts in Italian. The outcomes suggest which experimental settings can be most profitably used in this landscape, and show that significantly good results can be obtained.

关键词： Study Feature algorithm selection Italian Language

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algorithm selection for Preferred Extensions Enumeration 5

Algorithm Selection for Preferred Extensions Enumeration

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5th Conference on Computational Models of Argument (COMMA)

作者： Cerutti, Federico Giacomin, Massimiliano Vallati, Mauro Univ Aberdeen Kings Coll Dept Comp Sci Aberdeen AB9 1FX Scotland Univ Brescia Dept Informat Engn I-25121 Brescia Italy Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England

ISBN: (纸本)9781614994367;9781614994350

Enumerating semantics extensions in abstract argumentation is generally an intractable problem. For preferred semantics four algorithms have been recently proposed, AspartixM, NAD-Alg, PrefSAT and SCC-P, with significant runtime variations. This work is a first comprehensive exploration of the graph features and of their impact on the execution time of state-of-the-art preferred extensions enumeration algorithms. Following other areas of AI, we exploit empirical performance models, predictive models that relate instance features and algorithms performance. The result is an approach able to select the "best" algorithm for any Dung's argumentation framework with an accuracy, on the average, of the 80%. Moreover, we show that an algorithm selection approach based on classification can select the fastest algorithm in about the double of the number of cases where the most efficient algorithm outperforms the other ones (SCC-P), and about three times the number of cases of the second most efficient algorithm (PrefSAT).

关键词： argumentation semantics argumentation features algorithm selection

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