scholarly journals Classification of Leaf Diseases in Paddy Plant Based on Combined Approach of Texture and Colour Feature Extraction and Optimized Feature Selection

2021 ◽  
Author(s):  
Nithiya S ◽  
Annapurani K
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Combined Approach ◽  
Colour Feature

Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images

2019 ◽  
pp. 262-271 ◽  
Author(s):  
Alessandro S. Martins ◽  
Leandro A. Neves ◽  
Paulo R. Faria ◽  
Thaína A. A. Tosta ◽  
Daniel O. T. Bruno ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Polynomial Algorithm ◽  
Colour Feature

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2020 ◽  
pp. 707-725
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

An improved diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model

Sensor Review ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Deepika Kishor Nagthane ◽  
Archana M. Rajurkar
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Feature Selection ◽  
Decision Tree ◽  
Association Rule ◽  
Content Type ◽  
Tree Classifier ◽  
Mammogram Images ◽  
Diagnosis Technique

PurposeOne of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.Design/methodology/approachThe proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based ona priorialgorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.FindingsThe performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.Originality/valueThis paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2016 ◽  
pp. 17-36
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

scholarly journals A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Zena M. Hira ◽  
Duncan F. Gillies
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Extraction Methods ◽  
Computational Time ◽  
High Dimensional ◽  
Gene Expressions ◽  
Advantages And Disadvantages

We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

Automatic feature extraction and selection for classification of cyclical time series data

2017 ◽  
Vol 84 (3) ◽  
Author(s):  
Tizian Schneider ◽  
Nikolai Helwig ◽  
Andreas Schütze
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Feature Selection ◽  
Time Series Data ◽  
Use Cases ◽  
Series Data ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Cyclical Time

AbstractThe classification of cyclically recorded time series plays an important role in measurement technologies. Example use cases range from gas sensors combined with temperature cycled operation to condition monitoring using vibration analysis. Before machine learning can be applied to high dimensional cyclical time series data dimensionality reduction has to be performed to avoid the classifier suffering from overfitting and the “curse of dimensionality”. This paper introduces a set of four complementary feature extraction methods and three feature selection algorithms that can be applied in a fully automatized manner to reduce the number of dimensions. The feature extraction algorithms are capable of extracting characteristic features from cyclical time series catching information contained in local details and overall cycle shape as well as in frequency or time-frequency domain. The methods for feature selection are capable of selecting the most suitable features for linear and nonlinear classification. The methods were chosen to be applicable to a wide range of applications which is verified by testing the set of methods on four different use cases.

Land Cover/Land Use Classification of Urban Areas

1998 ◽  
Vol 12 (04) ◽  
pp. 475-489 ◽  
Author(s):  
Jukka Heikkonen ◽  
Aristide Varfis
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Feature Selection ◽  
Land Cover ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Self Organizing Map ◽  
Land Use Classification ◽  
K Nearest Neighbor

This paper proposes a method for remote sensing based land cover/land use classification of urban areas. The method consists of the following four main stages: feature extraction, feature coding, feature selection and classification. In the feature extraction stage, statistical, textural and Gabor features are computed within local image windows of different sizes and orientations to provide a wide variety of potential features for the classification. Then the features are encoded and normalized by means of the Self-Organizing Map algorithm. For feature selection a CART (Classification and Regression Trees) based algorithm was developed to select a subset of features for each class within the classification scheme at hand. The selected subset of features is not attached to any specific classifier. Any classifier capable of representing possible skewed and multi-modal feature distributions can be employed, such as multi-layer perceptron (MLP) or k-nearest neighbor (k-NN). The paper reports experiments in land cover/land use classification with the Landsat TM and ERS-1 SAR images gathered over the city of Lisbon to show the potentials of the proposed method.

scholarly journals Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection

2021 ◽  
Vol 38 (5) ◽  
pp. 1377-1383
Author(s):  
Revathi Vankayalapati ◽  
Akka Lakshmi Muddana
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Prediction Method ◽  
Tumor Detection ◽  
Brain Images ◽  
Tumor Recognition

In clinical practice and patient survival rates, early diagnosis of brain tumors plays a key role. Different forms of brain tumors and their properties and treatments are available. Therefore, tumor detection is complicated, time consuming and error-prone with manual brain tumor detection. Therefore, high-precision automated, computerized diagnostics are currently necessary. Feature extraction is a tumor prediction method for capturing the visual content of a picture. The extraction of features is the process through which the raw image is reduced and decisions like the pattern classification are facilitated. The MRI brain images are considered to be classified as a robust and more accurate classification that is able to serve as an expert assistant for healthcare practitioners. In this research, a new method for selecting and extracting features is introduced. The paper proposes to take into account the most important features for the classification of tumor and non-tumor cells using a Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection (DWLM-PWFS). This approach combines the tumor's intensity, texture, shape and diagnostic properties. The selection of features with the technique proposed is most helpful for analyzing data according to grouping class variable and ensuring reduced feature setting with high classification accuracy. In contrast to the conventional model, the model proposed is shown to be highly efficient in comparison with traditional models.

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