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Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections

PROGRESS IN ARTIFICIAL INTELLIGENCE 2013年第1期2卷 29-44页

作者： Garcia-Pedrajas, Nicolas Garcia-Osorio, Cesar Univ Cordoba Computat Intelligence & Bioinformat Res Grp Cordoba Spain Univ Burgos Adv Data Min Res & Bioinformat Learning Res Grp Burgos Spain

It has repeatedly been shown that most classification methods suffer from an imbalanced distribution of training instances among classes. Most learning algorithms expect an approximately even distribution of instances among the different classes and suffer, to different degrees, when that is not the case. Dealing with the class-imbalance problem is a difficult but relevant task, as many of the most interesting and challenging real-world problems have a very uneven class distribution. In this paper we present a new approach for dealing with class-imbalanced datasets based on a new boosting method for the construction of ensembles of classifiers. The approach is based on using the distribution of the weights given by a given boosting algorithm for obtaining a supervised projection. Then, the supervised projection is used to train the next classifier using a uniform distribution of the training instances. We tested our method using 35 class-imbalanced datasets and two different base classifiers: a decision tree and a support vector machine. The proposed methodology proved its usefulness achieving better accuracy than other methods both in terms of the geometric mean of specificity and sensibility and the area under the ROC curve.

关键词： Data mining Class-imbalanced problems Boosting real-coded genetic algorithms

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genetic algorithm with species and sexual selection

Genetic algorithm with species and sexual selection

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Raghuwanshi, M. M. Kakde, O. G. RCERT Chandrapur 442403 MS India VNIT Nagpur Maharashtra India

ISBN: (纸本)9781424400225

In this paper we have proposed new real coded genetic algorithm with species and sexual selection (GAS3). GAS3 is a distributed quasi steady-state real-coded genetic algorithm. GAS3 uses sex determination method (SDM) to determine the sex (male or female) of members in population. Each female member is considered as a niche in population and the species formation take place around these niches. Sexual selection strategy selects female and required number of male members from species to perform the recombination operation. The parent-centric recombination operators are used to generate offspring. If species is not performing well, then the merging of species to the nearby species takes place. Performance of GAS3 is tested on unimodal and multi-modal test functions. It got success in solving wide range of problems. Its performance is also compared with other real-coded genetic algorithm and evolutionary strategies.)

关键词： real-coded genetic algorithms parent-centric crossover operators chromosome differentiation Clustering sexual selection scheme distributed genetic algorithm niche and species

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IMAGE MATCHING ASSESSMENT OF ATTAINABLE TOPOLOGY VIA KRIGING-INTERPOLATED LEVEL-SETS

IMAGE MATCHING ASSESSMENT OF ATTAINABLE TOPOLOGY VIA KRIGING...

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ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC)

作者： Guirguis, David Aly, Mohamed Hamza, Karim Hegazi, Hesham Amer Univ Cairo Giza Egypt Cairo Univ Fac Engn Giza Egypt Amer Univ Cairo Dept Mech Engn Cairo Egypt Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Cairo Univ Fac Engn Mech Design & Prod Dept Giza Egypt

ISBN: (纸本)9780791846315

Level-set methods are domain classification techniques that are gaining popularity in the recent years for structural topology optimization. Level sets classify a domain into two or more categories (such as material and void) by examining the value of a scalar level-set function (LSF) defined in the entire design domain. In most level-set formulations, a large number of design variables, or degrees of freedom is used to define the LSF, which implicitly defines the structure. The large number of design variables makes non-gradient optimization techniques all but ineffective. Kriging-interpolated level sets (KLS) on the other hand are formulated with an objective to enable non gradient optimization by defining the design variables as the LSF values at few select locations (knot points) and using a Kriging model to interpolate the LSF in the rest of the design domain. A downside of concern when adopting KLS, is that using too few knot points may limit the capability to represent complex shapes, while using too many knot points may cause difficulty for non-gradient optimization. This paper presents a study of the effect of number and layout of the knot points in KLS on the capability to represent complex topologies in single and multi-component structures. Image matching error metrics are employed to assess the degree of mismatch between target topologies and those best-attainable via KLS. Results are presented in a catalogue-style in order to facilitate appropriate selection of knot-points by designers wishing to apply KLS for topology optimization.

关键词： Topology Optimization Level sets Kriging Image matching real-coded genetic algorithms

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Optimal design of a 6-DOF motion simulator based on formulating dynamic index

Optimal design of a 6-DOF motion simulator based on formulat...

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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Liu, Guojun Zheng, Shutao Liu, Xiaochu Han, Junwei Harbin Inst Technol Sch Mechatron Engn Harbin 150000 Heilongjiang Peoples R China Harbin Univ Sci & Technol Coll Mech & Power Engn Harbin 150080 Heilongjiang Peoples R China

ISBN: (纸本)9783037854488

Gough-Stewart platform (GSP) has been extensively used as motion simulators. In order to get good performance of the motion simulators, the dynamic characteristics are needed to be considered in the design process. This paper established a dynamic dexterity index based on the generalized mass matrix in the joint space, which is invariant under the change of units and has a physical meaning. The dynamic dexterity indexes can also be established in the similar way as other parallel robots with the only pure active joints. In the last, a motion simulator is optimized based on the dynamic dexterity index to satisfy the customer's requirements by using real-coded genetic algorithms.

关键词： Gough-Stewart Platform 6-DOF Motion Simulator Dynamic Dexterity Index Optimal Design Condition Number real-coded genetic algorithms

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MEDICAL DATA MINING USING BGA AND RGA FOR WEIGHTING OF FEATURES IN FUZZY K-NN CLASSIFICATION

MEDICAL DATA MINING USING BGA AND RGA FOR WEIGHTING OF FEATU...

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International Conference on Machine Learning and Cybernetics

作者： Tang, Ping-Hung Tseng, Ming-Hseng Chung Shan Med Univ Grad Inst Appl Informat Sci Taichung Taiwan Chung Shan Med Univ Sch Appl Informat Sci Taichung Taiwan

ISBN: (纸本)9781424447053

The k-nearest neighbor (k-NN) algorithm is commonly used in applications of classifiers and data mining and the related area due to its simplicity and effectiveness. In this study, all of features and optimal feature subsets with three features are investigated. For classification, crisp k-NN, fuzzy k-NN, and weighting fuzzy k-NN classifiers are compared. For weighting of features, two types of coding including binary-coded genetic algorithms (BGA) and real-coded genetic algorithms (BGA) are evaluated. Experiments are conducted on the Wisconsin diagnosis breast cancer (WDBC) dataset and the Pima (PIMA) Indians diabetes dataset, and the classification accuracy, false negative, and computation time are reported in this paper.

关键词： Crisp k-NN Fuzzy k-NN Weighting fuzzy k-NN Binary-coded genetic algorithms real-coded genetic algorithms

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Application of self-organizing maps to genetic algorithms

Application of self-organizing maps to genetic algorithms

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11th International Conference on Computer Aided Optimum Design of Structures

作者： Kan, S. Fei, Z. Kita, E. Nagoya Univ Grad Sch Informat Sci Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781845641856

This paper describes Self-Organizing Maps for genetic Algorithm (SOM-GA). In this algorithm, the search performance of a real-coded genetic algorithm (RCGA) is enhanced with self-organizing map (SOM). The SOM is trained with the information of the individuals in the population. Sub-populations are generated from a whole population by the help of the map. The RCGA search is performed in the sub-populations. The Rastrigin function is considered as a test problem. The search performance of SOM-GA is compared with that of the RCGA. The results show that the use of the sub-population search algorithm improves the local search performance of the RCGA and therefore, SOM-GA can find better solutions in shorter CPU time than RCGA.

关键词： real-coded genetic algorithms self-organizing maps Rastrigin function

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Optical Monitoring System Availability Optimization via Semi-Markov Processes and genetic algorithms

Optical Monitoring System Availability Optimization via Semi...

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54th Annual Reliability and Maintainability Symposium

作者： Moura, Marcio das Chagas Firmino, Paulo Renato A. Droguett, Enrique Lopez Jacinto, Carlos Magno Univ Fed Pernambuco Dept Prod Engn S-N Acad Helio Ramos Ave BR-50000000 Recife PE Brazil Petrobras Res Ctr CENPES BR-21941598 Rio De Janeiro Brazil

ISBN: (纸本)9781424414604

System availability optimization is one of the main issues to oil production managers: the greater the system availability the greater the production profits are. Provided that preventive maintenance actions promote rejuvenation impact on availability indicator, this paper proposes an approach to maximize the mean availability by identifying an optimal maintenance policy for downhole optical monitoring systems, which are modeled according to non-homogeneous semi-Markov processes. In order to solve the resulting optimization problem constrained by system performance costs, new real-coded GA operators are also presented. The proposed approach is exemplified by means of an application to a real scenario in onshore oil wells in Brazil.

关键词： non-homogeneous semi-Markov processes real-coded genetic algorithms system mean availability virtual age

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genetic Algorithm Designed for Solving Linear or Nonlinear Mixed-Integer Constrained Optimization Problems

Genetic Algorithm Designed for Solving Linear or Nonlinear M...

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International Conference on Advances in Soft Computing, Intelligent Systems and Applications (ASISA)

作者： Jalota, Hemant Thakur, Manoj Indian Inst Technol Mandi Mandi 175001 Himachal Prades India

ISBN: (纸本)9789811052729;9789811052712

genetic algorithms (GA) initially were not developed to handle the integer restriction or discrete values to the design variables. In the recent years, researchers has focused their work on developing/modifying GAs for handling integer/discrete variables. We have modified the BEX-PM algorithm developed by Thakur et al. [1] to solve the nonlinear constrained mixed-integer optimization problems. Twenty test problems have been used to conduct a comparative study to test the effectiveness of proposed algorithm (MI-BEXPM) with other similar algorithms (viz. MILXPM, RST2ANU, and AXNUM) in this class available in the literature. The efficacy of the results acquired through MI-BEXPM is compared with other algorithms on two well-known criteria. The performance of MI-BEXPM is also analysed and compared for solving real-life mixed-integer optimization problems with other methods available in the literature. It is found that MI-BEXPM is significantly superior to the algorithms considered in this work.

关键词： real-coded genetic algorithms Bounded exponential crossover Power mutation Mixed-integer optimization Constraint optimization

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Investigating Adaptive Mutation in the Generalized Generation Gap (G3) Algorithm for Unconstrained Global Optimization 07

Investigating Adaptive Mutation in the Generalized Generatio...

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22nd ACM Symposium on Applied Computing

作者： Teo, Jason Univ Malaysia Sabah Ctr AI Sch Engn & IT Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9781595934802

For function optimization problems in continuous search spaces, one of the main difficulties currently faced is that of locating high quality solutions. This problem is particularly pertinent for continuous multimodal problems where the quality rather than computational efficiency is more important as a test of the solver's ability to escape local optima and finding solutions near the global optimum [3]. Moreover, this difficulty is further compounded when the function involves large numbers of variables, which translates into a highly deceptive fitness landscape with very large numbers of local optima [2].

关键词： Adaptive Mutation G3 real-coded genetic algorithms

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A genetic algorithmic approach on a deterministic inventory model for deteriorating items with shortages

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INFOR 2005年第3期43卷 271-282页

作者： Roy, S Bhunia, AK Mukhopadhyay, S BB Coll Dept Comp Applicat Asansol 713303 India Univ Burdwan Dept Math Burdwan 713104 W Bengal India Univ Burdwan Dept Comp Sci Burdwan 713104 W Bengal India

The purpose of this research is to determine an optimal solution of a deterministic inventory model of single deteriorating items with a constant rate of deterioration. In this model, the demand rate is a ramp type function of time. Shortages are allowed and partially backlogged. During the shortage period, the backlogging rate is a variable which depends on the length of the waiting time over the replenishment period. The mathematical formulation of the problem indicates that the model is a non-linear constrained optimization problem. Considering the complexity of solving such a model (for getting global optima, not local optima, as it is a decision making problem.), a real-coded genetic algorithms (GAs) with Random Stochastic Sampling selection (with replacement), whole arithmetic crossover and mutation has been developed. In the algorithm, mutation is carried out for the fine tuning capabilities of the system by non-uniform operators whose action depends on the age of the population. The proposed model has been solved using this real-coded GA as well as Generalised Reduced Gradient (GRG) Method. Finally, the results are illustrated with the help of a numerical example and sensitivity analysis of the optimal solution with respect to the different parameters of the system is carried out.

关键词： real-coded genetic algorithms non-uniform mutation crossover selection inventory system deterioration partial backlogging ramp type demand

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