A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with ApplicationsPublished 6 May 2006
Rahul Karthik Sivagaminathan, Sreeram Ramakrishnan
Citations258
SJR quartileQ1
SJR score1.85
SNIP2.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A hybrid method based on ant colony optimization and artificial neural networks (ANNs) to address feature selection is presented, yielding promising results.
Abstract
One of the significant research problems in multivariate analysis is the selection of a subset of input variables that can predict the desired output with an acceptable level of accuracy. This goal is attained through the elimination of the variables that produce noise or, are strictly correlated with other already selected variables. Feature subset selection (selection of the input variables) is important in correlation analysis and in the field of classification and modeling. This paper presents a hybrid method based on ant colony optimization and artificial neural networks (ANNs) to address feature selection. The proposed hybrid model is demonstrated using data sets from the domain of medical diagnosis, yielding promising results.
Keywords
Computer Science
IEEE Transactions on Evolutionary ComputationAnt colony system: a cooperative learning approach to the traveling salesman problem
7,994 Citations1997Marco Dorigo, Luca Maria Gambardella
The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Cambridge University Press eBooksPattern Recognition and Neural Networks
6,468 Citations1996B. D. Ripley
Artificial IntelligenceSelection of relevant features and examples in machine learning
3,295 Citations1997Avrim Blum, Pat Langley
This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area.
Pattern Recognition LettersFloating search methods in feature selection
3,070 Citations1994Pavel Pudil, Jana Novovičová +1 more
Artificial LifeAnt Algorithms for Discrete Optimization
2,819 Citations1999Marco Dorigo, Gianni A. Di +1 more
An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
Elsevier eBooksIrrelevant Features and the Subset Selection Problem
2,374 Citations1994George H. John, Ron Kohavi +1 more
A method for feature subset selection using cross-validation that is applicable to any induction algorithm is described, and experiments conducted with ID3 and C4.5 on artificial and real datasets are discussed.
New Ideas In Optimization
2,135 Citations1999David Corne, Marco Dorigo +5 more
The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
National Conference on Artificial IntelligenceThe feature selection problem: traditional methods and a new algorithm
1,833 Citations1992Kenji Kira, Larry Rendell
A new algorithm Rellef is introduced which selects relevant features using a statistical method and is accurate even if features interact, and is noise-tolerant, suggesting a practical approach to feature selection for real-world problems.
IEEE Intelligent Systems and their ApplicationsFeature subset selection using a genetic algorithm
1,363 Citations1998Jihoon Yang, Vasant Honavar
The authors' approach uses a genetic algorithm to select subsets of attributes or features to represent the patterns to be classified, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
IEEE Transactions on ComputersA Branch and Bound Algorithm for Feature Subset Selection
1,240 Citations1977Narendra, Fukunaga
A feature subset selection algorithm based on branch and bound techniques is developed to select the best subset of m features from an n-feature set with the computational effort of evaluating only 6000 subsets.
Journal of ChemometricsGenetic algorithms as a strategy for feature selection
824 Citations1992Riccardo Leardi, Raffaella Boggia +1 more
The subsets of variables selected by genetic algorithms are generally more efficient than those obtained by classical methods of feature selection, since they can produce a better result by using a lower number of features.
Adaptive BehaviorAnt-Based Load Balancing in Telecommunications Networks
744 Citations1997Ruud Schoonderwoerd, Owen Holland +2 more
A novel method of achieving load balancing in telecommunications networks using ant-based control, which is shown to result in fewer call failures than the other methods, while exhibiting many attractive features of distributed control.
Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles)Ant system for Job-shop scheduling
618 Citations1994Alberto Colorni, Marco Dorigo +2 more
This paper shows how a new heuristic called ant system, in which the search task is distributed over many simple, loosely interacting agents, can be successfully applied to find good solutions of job-shop scheduling problems.
National Conference on Artificial IntelligenceLearning with many irrelevant features
611 Citations1991Hussein Almuallim, Thomas G. Dietterich
It is demonstrated that CaS protects against oxidative stress‐induced keratinocyte cell death in part through the activation of Nrf2‐mediated HO‐1 induction via the PI3K/Akt and/or PKC pathways, but not MAPK signaling.
Elsevier eBooksGreedy Attribute Selection
541 Citations1994Rich Caruana, Dayne Freitag
Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance, and a caching scheme is presented that makes attribute hillClimbing more practical computationally.
Journal of the Operational Research SocietyAnts can colour graphs
501 Citations1997Davide Costa, Alain Hertz
An evolutionary search procedure for tackling assignment type problems that repeatedly constructs feasible solutions of the problem under study by taking account of two complementary notions, namely the trace factor and the desirability factor is presented.
Applying the ANT System to the Vehicle Routing Problem
412 Citations1999B. Bullnheimer, Richard F. Hartl +1 more
A recently proposed metaheuristic, the Ant System, is used to solve the Vehicle Routing Problem in its basic form, i.e., with capacity and distance restrictions, one central depot and identical vehicles.
Artificial Intelligence in MedicineLung cancer cell identification based on artificial neural network ensembles
352 Citations2002Zhi‐Hua Zhou, Yuan Jiang +2 more
An automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed.
Oxford University Press eBooksFrom Natural to Artificial Swarm Intelligence
281 Citations1999Eric Bonabeau, Marco Dorigo +1 more
Elsevier eBooksUsing Decision Trees to Improve Case-Based Learning
252 Citations1993Claire Cardie
Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge.
Further Research on Feature Selection and Classification Using Genetic Algorithms
243 Citations1993William F. Punch, Erik D. Goodman +4 more
This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiications by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals within groups and to separate groups from one another.
An Ant Approach to the Flow Shop Problem
183 Citations1998Thomas Stützle
ACO is a new algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems.
IEEE Transactions on Information TheoryLearning pattern classification-a survey
162 Citations1998Sanjeev R. Kulkarni, Gábor Lugosi +1 more
This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications.
Open Publications Of UTS Scholars (University of Technology Sydney)Feature Subset Selection Using Ant Colony Optimization
161 Citations2008Ahmed Al-Ani
A novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented, a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources.
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
102 Citations1997Luca Maria Gambardella, Marco Dorigo
Experimental results on a set of twenty-three test problems taken from the TSPLIB show that HAS-SOP outperforms existing methods both in terms of solution quality and computation time.
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
94 Citations2003Christian Blum, Michaël Sampels
Different pheromone representations taken from the literature are compared with a new approach to the FOP shop scheduling problem which is a general scheduling problem including job shop scheduling, open shop scheduling and mixed shop scheduling as special cases.
Journal of Intelligent Information SystemsFeature Subset Selection within a Simulated Annealing Data Mining Algorithm
86 Citations1997Justin C. W. Debuse, V. J. Rayward‐Smith
It is shown that a substantial reduction in the number of features may be achieved together with animation in the performance of the data mining system, and an entropic measure is developed, based upon the information gain approach used within ID3 and C4.5 to build trees.
Feature Subset Selection by Using Sorted Feature Relevance.
27 Citations2002Olcay Boz
It is shown that the new method chooses good subsets by searching fewer s tates than the existing methods, and speeds up the search considerably for most of the problem domains.
Lecture notes in computer scienceMetaheuristics for Group Shop Scheduling
21 Citations2002Michaël Sampels, Christian Blum +2 more
This paper guarantees a fair comparison by a common definition of neighborhood in the search space, by using the same data structure, programming language and compiler, and by running the algorithms on the same hardware.
An Evolutionary Computational Approach to Probabilistic Neural Network with Application to Hepatic Cancer Diagnosis
19 Citations2005Florin Gorunescu, Marina Gorunescu +2 more
An evolutionary approach based on genetic algorithm to optimise the search of the smoothing parameter in a modified probabilistic neural network is introduced and the computational results showed the viability of this hybrid approach to determine the optimum diagnosis for hepatic diseases.
Letting ants labeling point features [sic.: for 'labeling' read 'label']
19 Citations2003Michael Schreyer, Günther R. Raidl
Empirical results indicate that the ACS reliably identifies high-quality solutions which are in many cases better than those of a state-of-the-art genetic algorithm for point-feature labeling.
Letting Ants Labeling Point Features
18 Citations2002Michael Schreyer
Empirical results indicate that the ACS reliablyifies high-quality solutions which are in many cases better than those of a state-of-the-art genetic algorithm for point feature point features.
IEEE Transactions on Information TheoryLearning decision rules for pattern classification under a family of probability measures
15 Citations1997Sanjeev R. Kulkarni, M. Vidyasagar
Lecture notes in computer scienceA COMPARATIVE STUDY OF ANT-BASED OPTIMIZATION FOR DYNAMIC ROUTING
9 Citations2001Kwang Mong Sim, Weng Hong Sun
This paper brings together a collection of ACO algorithms discussed their features, strength and weaknesses and introduces the general idea of Ant-based algorithms with a focus on Ant Colony Optimization (ACO).
LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Pattern recognition in medical images using neural networks
5 Citations2001Laura Cristina Lanzarini, Armando Eduardo De Giusti
The proposal of this research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion, even though similar applications in other fields are not discarded.