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Increasing the efficiency of quicksort using a neural network based algorithm selection model

INFORMATION SCIENCES 2013年 229卷 94-105页

作者： Kocamaz, Ugur Erkin Sakarya Univ Vocat Sch Geyve Dept Comp Programming TR-54700 Geyve Sakarya Turkey

Quicksort is one of the most popular sorting algorithms, it is based on a divide-and-conquer technique and has a wide acceptance as the fastest general-purpose sorting technique. Though it is successful in separating large partitions into small ones, quicksort runs slowly when it processes its small partitions, for which completing the sorting through using a different sorting algorithm is much plausible solution. This variant minimizes the overall execution time but it switches to a constant sorting algorithm at a constant cut-off point. To cope with this constancy problem, it has been suggested that a dynamic model which can choose the fastest sorting algorithm for the small partitions. The model includes continuation with quicksort so that the cut-off point is also more flexible. To implement this with an intelligent algorithm selection model, artificial neural networks are preferred due to their non-comparison, constant-time and low-cost architecture features. In spite of the fact that finding the best sorting algorithm by using a neural network causes some extra computational time, the gain in overall execution time is greater. As a result, a faster variant of quicksort has been implemented by using artificial neural network based algorithm selection approach. Experimental results of the proposed algorithm and the Several other fast sorting algorithms have been presented, compared and discussed. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Neural algorithm selection Sorting algorithm selection Quicksort Sorting algorithm selection Neural networks

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Anytime Automatic algorithm selection for the Multi-Agent Path Finding Problem

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IEEE ACCESS 2024年 12卷 62177-62188页

作者： Zapata, Angelo Godoy, Julio Asin-Acha, Roberto Univ Concepcion Dept Comp Sci Concepcion 4070409 Chile Univ Tecn Federico Santa Maria Dept Informat Valparaiso 234000 Chile

In this study, we propose and develop a Machine Learning-based metasolver for the Multi-Agent Path Finding (MAPF) problem, with the aim of selecting the most suitable solver based on the specific characteristics of the problem and a user-provided time constraint. The approach aims to improve the performance of the best-performing solver on average and approximate the performance of a perfect selector. To achieve this, a comprehensive and diverse dataset was compiled, and state-of-the-art algorithms were selected and modified to efficiently handle the time constraint. Also, relevant features were identified, and a precise and robust Machine Learning model was constructed using the XGBoost algorithm. The model was evaluated and compared against other state-of-the-art methods. The results demonstrate that the proposed approach is effective and consistent, outperforming the Single Best Solver and approximating the performance of the Virtual Best Solver.

关键词： algorithm design and analysis Multi-agent systems Path planning Time factors Approximation algorithms Performance evaluation algorithm selection multi-agent path finding machine learning

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Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 62卷 767-776页

作者： Li, Li Wang, Yong Xu, Ying Lin, Kuo-Yi Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China Beijing Wodong Tianjun Informat Technol Co Ltd Shanghai Branch Beijing Peoples R China

As more and more novel algorithms being proposed, it is difficult for engineers to choose suitable algorithms to solve engineering problems in manufacturing systems. According to the No Free Lunch theorems, there is no universal algorithm applicable to all problems. To solve the problem, this paper proposes a meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems which are common in manufacturing. The proposed framework consists of three parts: dataset module, classification algorithms module and recommendation module. Firstly, 32 dataset characteristic measurement indicators are extracted based on meta-learning from 34 popular datasets. Then 13 common classification algorithms are applied in each dataset for algorithm optimizing. Through comparative analysis, it is found that the bat algorithm is better than the differential evolution algorithm in parameter optimization. Finally, in recommendation module, the relatively better algorithm is recommended to the new dataset according to the similarity between the new dataset and the reference datasets, which is measured by four distance methods. The result shows that the recommendation accuracy and hit rate of the new dataset are not less than 84.10 % and 85.29 % respectively, which can provide a good algorithm recommendation for engineers.

关键词： algorithm selection Meta-learning Classification problems Characteristic measurement indicators Similarity

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algorithm selection Framework for Cyber Attack Detection 2

Algorithm Selection Framework for Cyber Attack Detection

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2nd ACM Workshop on Wireless Security and Machine Learning (WiseML)

作者： Chale, Marc Bastian, Nathaniel D. Weir, Jeffery Air Force Inst Technol Wright Patterson AFB OH 45433 USA Army Cyber Inst West Point NY USA

ISBN: (纸本)9781450380072

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.

关键词： machine learning algorithm selection meta learning feature engineering cybersecurity

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algorithm selection for the Capacitated Vehicle Routing Problem Using Machine Learning Classifiers

Algorithm Selection for the Capacitated Vehicle Routing Prob...

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作者： Fellers, Justin C. University of Rhode Island

学位级别：M.S., Master of Science/Master of Surgery

The Vehicle Routing Problem (VRP) is a widely known NP-Hard operations research problem endowed with a wide range of heuristic algorithms generated from decades of global research. These heuristics provide solutions within a reasonable run time, but at some expense to optimality. The literature further suggests heuristic performance in one class of problems comes at a cost in performance to other classes. This study aimed to develop methods for selecting the best heuristic from a defined set to solve an arbitrary VRP instance. Known as the algorithm selection problem, this study implemented supervised machine learning techniques to construct prediction models based upon instance characteristics. These models were evaluated by metrics commonly found in both algorithm selection and machine learning studies. Built from a set of 23 features and a portfolio of four varied heuristics, the leading model correctly predicted the best algorithm with 79% accuracy despite the single best heuristic occurring only49% of the time. Models were constructed using a custom problem space of 5,000 VRP instances developed organically by novel methods adapted from the literature. Adequacy of the problem space, regarding its range of difficulties and sufficiency of size, was also explored. The results indicate the problem space was appropriately diverse and the prediction models, which were developed by learning algorithms using provided data, are unlikely to improve accuracy if given more data.

关键词： algorithm selection Machine learning Vehicle routing

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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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A review on preprocessing algorithm selection with meta-learning

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第1期66卷 1-28页

作者： Pio, Pedro B. Rivolli, Adriano de Carvalho, Andre C. P. L. F. Garcia, Luis P. F. Univ Brasilia UnB Dept Comp Sci CIC BR-70910900 Brasilia DF Brazil Fed Univ Technol UTFPR Campus Cornelio Procopio BR-86300000 Cornelio Procopio Parana Brazil Univ Sao Paulo Inst Math & Comp Sci ICMC Ave Trabalhador Sao Carlense 400 BR-13560970 Sao Carlos SP Brazil

Several AutoML tools aim to facilitate the usability of machine learning algorithms, automatically recommending algorithms using techniques such as meta-learning, grid search, and genetic programming. However, the preprocessing step is usually not well handled by those tools. Thus, in this work, we present a systematic review of preprocessing algorithms selection with meta-learning, aiming to find the state of the art in this field. To perform this task, we acquired 450 references, of which we selected 37 to be evaluated and analyzed according to a set of questions earlier defined. Thus, we managed to identify information such as what was published on the subject;the topics more often presented in those works;the most frequently recommended preprocessing algorithms;the most used features selected to extract information for the meta-learning;the machine learning algorithms employed as meta-learners and base-learners in those works;and the performance metrics that are chosen as the target of the applications.

关键词： Systematic review Meta-learning algorithm selection Preprocessing

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On learning algorithm selection for classification

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APPLIED SOFT COMPUTING 2006年第2期6卷 119-138页

作者： Ali, S Smith, KA Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

This paper introduces a new method for learning algorithm evaluation and selection, with empirical results based on classification. The empirical study has been conducted among 8 algorithms/classifiers with 100 different classification problems. We evaluate the algorithms' performance in terms of a variety of accuracy and complexity measures. Consistent with the No Free Lunch theorem, we do not expect to identify the single algorithm that performs best on all datasets. Rather, we aim to determine the characteristics of datasets that lend themselves to superior modelling by certain learning algorithms. Our empirical results are used to generate rules, using the rule-based learning algorithm C5.0, to describe which types of algorithms are suited to solving which types of classification problems. Most of the rules are generated with a high confidence rating. (C) 2005 Elsevier B. V. All rights reserved.

关键词： algorithm selection classification No Free Lunch theorem

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Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering

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INFORMATION SCIENCES 2018年 423卷 128-144页

作者： Cunha, Tiago Soares, Carlos de Carvalho, Andre C. P. L. F. Univ Porto Fac Engn Oporto Portugal Univ Sao Paulo ICMC Sao Carlos SP Brazil

The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Metalearning algorithm selection Recommendation system Collaborative Filtering

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A study on the effects of normalized TSP features for automated algorithm selection

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THEORETICAL COMPUTER SCIENCE 2023年第PartB期940卷 123-145页

作者： Heins, Jonathan Bossek, Jakob Pohl, Janina Seiler, Moritz Trautmann, Heike Kerschke, Pascal Big Data Analyt Transportat TU Dresden Dresden Germany Rhein Westfal TH Aachen Methodol Aachen Germany Univ Munster Stat & Optimizat Munster Germany

Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Feature normalization algorithm selection Traveling salesperson problem

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