A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation

Ma, Lei; Li, Manchun; Gao, Yu; Tan, Cheng; Ma, Xiaowei; Qu, Le’an

doi:10.1109/lgrs.2016.2645710

Cited by 67 publications

(31 citation statements)

References 20 publications

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“…When comparing seven feature selection algorithms for land cover classification using the SVM and RF methods, Ma et al [67] found that the SVM Recursive Feature Elimination (SVM-RFE) wrapper was appropriate for both classifiers, SVM and RF. A novel wrapper approach has also been suggested by Ma et al [72], which involves the integration of: (i) feature importance rank using gain ratio; and (ii) feature subset evaluation using a polygon-based tenfold cross-validation within a support vector machine (SVM) classifier. As reported by the authors, this approach yielded promising results, considerably increasing the final classification overall accuracy.…”

Section: ρG−ρr ρG+ρrmentioning

confidence: 99%

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

et al. 2017

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Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification.

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Section: ρG−ρr ρG+ρrmentioning

confidence: 99%

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

et al. 2017

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“…The rankings can then be assessed to decide whether to keep or discard the variable from the analysis [25]. The wrapper method, on the contrary, takes into account a subset of features and com-pares between different combinations of attributes to assign scores to the features [26]. The embedded method is slightly more complicated, since the learning method usually decides which features are best for a model while the model is being built [27].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Risk prediction in life insurance industry using supervised learning algorithms

Boodhun

Jayabalan

2018

Complex Intell. Syst.

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Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants. Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models.

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“…Wang et al proposed a featureselection method with PSO to remove the irrelevant and redundant features from the high dimensional space to improve the accuracy of the classification algorithm [11]. Ma et al presented a wrapper-based feature-selection method using IG ratio measure with support vector machine and machine-learning algorithm to select the significant features from the data set to improve the accuracy in classification [12]. Huda [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, the wrapper-based feature-selection method produces higher accuracy for the specific task [12,21]. Hence, the wrapper-based feature selection approach is preferred by the researchers where the recognition task is predefined.…”

Section: Literature Reviewmentioning

confidence: 99%

Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

Singh¹,

Priyadharshini²,

Leavline³

2018

IJCNIS

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In the digital era, cloud computing plays a significant role in scalable resource sharing to carry out seamless computing and information sharing. Securing the data, resources, applications and infrastructure of the cloud is a challenging task among the researchers. To secure the cloud, cloud security controls are deployed in the cloud computing environment. The cloud security controls are roughly classified as deterrent controls, preventive controls, detective controls and corrective controls. Among these, detective controls are significantly contributing for cloud security by detecting the possible intrusions to prevent the cloud environment from the possible attacks. This detective control mechanism is established using intrusion detection system (IDS). The detecting accuracy of the IDS greatly depends on the network traffic data that is employed to develop the IDS using machine-learning algorithm. Hence, this paper proposed a cuckoo optimisation-based method to preprocess the network traffic data for improving the detection accuracy of the IDS for cloud security. The performance of the proposed algorithm is compared with the existing algorithms, and it is identified that the proposed algorithm performs better than the other algorithms compared.

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A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation

Cited by 67 publications

References 20 publications

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Risk prediction in life insurance industry using supervised learning algorithms

Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

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