scholarly journals HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 271-279
Author(s):  
E. Tamimi ◽  
H. Ebadi ◽  
A. Kiani
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Computational Time ◽  
Support Vector ◽  
Building Detection ◽  
Svm Classification ◽  
Spectral Bands ◽  
Object Based ◽  
High Resolution Images ◽  
Independent Features

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2995-2998
Author(s):  
Xiaoqin Zhang ◽  
Guo Jun Jia
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Algorithm Selection ◽  
Data Set ◽  
Network Intrusion

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

scholarly journals Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

2019 ◽  
Vol 125 ◽  
pp. 23012
Author(s):  
Edy Subowo ◽  
Eko Sediyono ◽  
Farikhin
Keyword(s):  
Ant Colony Algorithm ◽  
Fire Suppression ◽  
Computation Time ◽  
Ant Colony ◽  
Training Data ◽  
Traffic Information ◽  
Support Vector ◽  
Weighting Method ◽  
Shortest Distance ◽  
Simple Additive Weighting

Combining the search method for fire suppression routes with ant colony algorithms and methods of analyzing twitter events on the highway is the basis of the problems to be studied. The results of the twitter data feature extraction are classified with Support Vector Machine after it is implemented in the Simple Additive Weighting method in calculating path weights with criteria of distance, congestion, multiple branching, and many holes. Line weights are also used as initial pheromone values. The C-means method is used to group the weights of each path and distance so that the path with the lowest weight and the shortest distance that will be simulated using the Ant Colony. The validation results with cross fold on SVM with linear kernels produce the greatest accuracy value is 97.93% for training data distribution: test data 6: 4. The simulation of the selection of the damkar car path from Feather to Pleburan with Ant Colony obtained 50 seconds of computation time, whereas with Ant Colony with Clustering the computation time was 15, resulting in a reduction in computing of 35. Ant colony with MinMax optimization gives the best computation time of 14.47 seconds with 100 iterations and 10 nodes.

scholarly journals Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

2016 ◽  
Vol 24 (3) ◽  
pp. 385-409 ◽  
Author(s):  
Fernando E. B. Otero ◽  
Alex A. Freitas
Keyword(s):  
Ant Colony Algorithm ◽  
Predictive Accuracy ◽  
Ant Colony ◽  
Support Vector ◽  
Classification Rules ◽  
Class Prediction ◽  
Vector Machines ◽  
Size Measure ◽  
The Impact

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

A flexibility method for structural damage identification using continuous ant colony optimization

2015 ◽  
Vol 11 (2) ◽  
pp. 186-201 ◽  
Author(s):  
Maryam Daei ◽  
S. Hamid Mirmohammadi
Keyword(s):  
Experimental Data ◽  
Damage Detection ◽  
Ant Colony Optimization ◽  
Structural Damage ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Computational Time ◽  
Content Type ◽  
Flexibility Matrix

Purpose – The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental techniques for damage detection are expensive and require that the vicinity of the damage is known and readily accessible; therefore several methods intend to detect damage based on numerical model and by means of minimum experimental data about dynamic properties or response of damaged structures. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the damage detection problem is formulated as an optimization problem such as to obtain the minimum difference between the numerical and experimental variables, and then a modified ant colony optimization (ACO) algorithm is proposed for solving this optimization problem. In the proposed algorithm, the structural damage is detected by using dynamically measured flexibility matrix, since the flexibility matrix of the structure can be estimated from only the first few modes. The continuous version of ACO is employed as a probabilistic technique for solving this computational problem. Findings – Compared to classical methods, one of the main strengths of this meta-heuristic method is the generally better robustness in achieving global optimum. The efficiency of the proposed algorithm is illustrated by numerical examples. The proposed method enables the deduction of the extent and location of structural damage, while using short computational time and resulting good accuracy. Originality/value – Finding accurate results by means of minimum experimental data, while using short computational time is the final goal of all researches in the structural damage detection methods. In this paper, it gains by applying flexibility matrix in the definition of objective function, and also via using continuous ant colony algorithm as a powerful meta-heuristic techniques in the constrained nonlinear optimization problem.

scholarly journals LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS

2017 ◽  
Vol 10 (1) ◽  
pp. 43 ◽  
Author(s):  
Nursuci Putri Husain ◽  
Nursanti Novi Arisa ◽  
Putri Nur Rahayu ◽  
Agus Zainal Arifin ◽  
Darlis Herumurti
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Classification Accuracy ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Support Vector ◽  
Optimal Parameters ◽  
Local Fisher Discriminant Analysis ◽  
Vector Machines ◽  
Improved Ant Colony Algorithm

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.

Natural Image Classification Based on Improved Support Vector Machine

2011 ◽  
Vol 58-60 ◽  
pp. 2387-2391
Author(s):  
Ying Jian Qi ◽  
Zhi Wei Ou ◽  
Bin Zhang ◽  
Ting Zhan Liu ◽  
Ying Li
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Ant Colony Algorithm ◽  
Image Representation ◽  
Image Features ◽  
Ant Colony ◽  
Function Parameter ◽  
Support Vector ◽  
Natural Image ◽  
Improved Support Vector Machine

Local image representation based natural image classification is an important task. SIFT descriptors and bag-of-visterm (BOV)method have achieved very good results. Many studies focused on improving the representation of the image, and then use the support vector machine to classify and identify the image category. However, due to support vector machine its own characteristics, it shows inflexible and slower convergence rate for large samples，with the selection of parameters influencing the results for the algorithm very much. Therefore, this paper will use the improved support vector machine algorithm be based on ant colony algorithm in classification step. The method adopt dense SIFT descriptors to describe image features and then use two levels BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks.

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

2009 ◽  
Vol 224 (1) ◽  
pp. 217-229 ◽  
Author(s):  
X L Zhang ◽  
X F Chen ◽  
Z J He
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Parameter Optimization ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Optimal Parameters ◽  
Vector Machines ◽  
Diagnosis Method

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

Transformer Fault Diagnosis Method Based on Support Vector Machine and Ant Colony

2013 ◽  
Vol 659 ◽  
pp. 54-58 ◽  
Author(s):  
Li Li Mo
Keyword(s):  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Cross Validation ◽  
Gas Analysis ◽  
Ant Colony ◽  
Support Vector ◽  
Dissolved Gas ◽  
Wide Range ◽  
Diagnosis Method ◽  
Transformer Fault

For transformer fault diagnosis of the IEC three-ratio is an effective method in the dissolved gas analysis (DGA). But it does not offer completely objective, accurate diagnosis for all the faults. Aiming at parameters are confirmed by the cross validation, using the ant colony algorithm, the ACSVM-IEC method for the transformer fault diagnosis is proposed. Experimental results show that the proposed algorithm in this paper that can find out the optimum accurately in a wide range. The proposed approach is robust and practical for transformer fault diagnosis.

Research on SVM Cell Concentration Prediction of Erythromycin Fermentation Process Based on Ant Colony Optimization

2013 ◽  
Vol 734-737 ◽  
pp. 2998-3002
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Fermentation Process ◽  
Ant Colony Algorithm ◽  
Cell Concentration ◽  
Ant Colony ◽  
Support Vector ◽  
Nonlinear Complexity ◽  
Svm Model ◽  
Concentration Prediction

Due to disadvantages of nonlinear, complexity and uncertainty of fermentation process, a research on cell concentration prediction of erythromycin fermentation process was carried out. Combining the optimization ability of ant colony algorithm and the regression ability of support vector machine, an ACO-SVM model is built. Case study shows that, the model is more accurate and more effective for the cell concentration prediction than ANN and SVM model.

scholarly journals Fault diagnosis of gearbox based on ant colony algorithm optimized support vector machine

2021 ◽  
Vol 2137 (1) ◽  
pp. 012068
Author(s):  
Shilin Sun ◽  
Renxiang Lu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Feature Vector ◽  
Vibration Signal ◽  
Ant Colony ◽  
Fault Classification ◽  
Function Parameter ◽  
Support Vector ◽  
Factor C

Abstract The kernel function parameter g and penalty factor c in Support Vector Machine (SVM) will have an important impact on the fault classification and performance of the support vector machine. Based on this, a fault analysis and diagnosis model using ant colony algorithm to optimize support vector machine is proposed to improve the accuracy of gearbox fault diagnosis. First, the collected original vibration signal is decomposed by EEMD to obtain the modal function component IMF, and then the energy entropy of the IMF component is calculated as the feature vector of the original vibration signal. Finally, the feature vector is input to the support vector optimized by the ant colony algorithm identify and classify in the machine, and finally get the diagnosis result. Comparing ACO-SVM with SVM, the experimental results prove that the ACO-SVM model has a higher fault diagnosis rate, stronger optimization ability, and faster convergence speed.

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