Forest Type Classification: A Hybrid NN-GA Model Based Approach
01 Jan 2016-pp 227-236
TL;DR: The authors have proposed a GA trained Neural Network classifier to tackle the task of classify tree species and one mixed forest class using geographically weighted variables calculated for Cryptomeria japonica and Chamaecyparis obtusa.
Abstract: Recent researches have used geographically weighted variables calculated for two tree species, Cryptomeria japonica (Sugi, or Japanese Cedar) and Chamaecyparis obtusa (Hinoki, or Japanese Cypress) to classify the two species and one mixed forest class. In machine learning context it has been found to be difficult to predict that a pixel belongs to a specific class in a heterogeneous landscape image, especially in forest images, as ground features of nearly located pixel of different classes have very similar spectral characteristics. In the present work the authors have proposed a GA trained Neural Network classifier to tackle the task. The local search based traditional weight optimization algorithms may get trapped in local optima and may be poor in training the network. NN trained with GA (NN-GA) overcomes the problem by gradually optimizing the input weight vector of the NN. The performance of NN-GA has been compared with NN, SVM and Random Forest classifiers in terms of performance measures like accuracy, precision, recall, F-Measure and Kappa Statistic. The results have been found to be satisfactory and a reasonable improvement has been made over the existing performances in the literature by using NN-GA.
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TL;DR: A novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF.
Abstract: A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN’s input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system performance was compared to the multilayer perceptron feed-forward neural network (MLP-FFN) classifier. Results proved the dominance of the proposed method with achieved accuracy of 90.91 %.
56 citations
01 Jan 2018
50 citations
TL;DR: A modified bag-of-features method has been proposed to select the most promising genes in the classification process and results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.
Abstract: Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.
43 citations
Cites background or methods from "Forest Type Classification: A Hybri..."
...Previously, several recently developed meta-heuristic algorithms have been employed to train ANNs apart from traditional ones, such as genetic algorithm [40], particle swarm optimization [32], and the non-dominated sorting genetic algorithm [41]....
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...ter quality prediction [36], electrical energy output prediction [33], dengue fever classification [42], and environmental application [40]....
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01 Aug 2017
TL;DR: Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features, which has been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure.
Abstract: Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.
39 citations
Cites background from "Forest Type Classification: A Hybri..."
...Early stage detection of the disease type is important since appropriate treatments can be applied based on the type of the disease [3]....
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01 Oct 2017
TL;DR: Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model in terms of testing phase confusion matrix based performance measuring metrics.
Abstract: The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.
36 citations
References
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TL;DR: In this article, the authors evaluated measures for making comparisons of errors across time series and found that the median absolute error of a given method to that from the random walk forecast is not reliable, and therefore inappropriate for comparing accuracy across series.
Abstract: This study evaluated measures for making comparisons of errors across time series. We analyzed 90 annual and 101 quarterly economic time series. We judged error measures on reliability, construct validity, sensitivity to small changes, protection against outliers, and their relationship to decision making. The results lead us to recommend the Geometric Mean of the Relative Absolute Error (GMRAE) when the task involves calibrating a model for a set of time series. The GMRAE compares the absolute error of a given method to that from the random walk forecast. For selecting the most accurate methods, we recommend the Median RAE (MdRAE) when few series are available and the Median Absolute Percentage Error (MdAPE) otherwise. The Root Mean Square Error (RMSE) is not reliable, and is therefore inappropriate for comparing accuracy across series.
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TL;DR: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent.
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Book•
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TL;DR: This new text has been designed to present the concepts of artificial neural networks in a concise and logical manner for computer engineering students.
Abstract: From the Publisher:
This new text has been designed to present the concepts of artificial neural networks in a concise and logical manner for your computer engineering students.
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TL;DR: Remote sensing is an attractive source of data for land cover mapping applications, and when pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover.
Abstract: Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, ho...
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TL;DR: The searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set and the proposed technique is able to distinguish some characteristic landcover types in the image.
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