scholarly journals Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

2019 ◽  
Vol 15 (12) ◽  
pp. 4
Author(s):  
Mohammad Subhi Al-batah
Keyword(s):  
Cervical Cancer ◽  
Feature Extraction ◽  
Feature Selection ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Training Data ◽  
Decision Table ◽  
Features Selection ◽  
Feature Extraction Method ◽  
Automatic Feature Extraction

<p class="0abstract">In this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classification. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification.</p>

Near Optimal Approach to General Manufacturing Feature Extraction

1995 ◽  
Author(s):  
Jian (John) Dong ◽  
Sreedharan Vijayan
Keyword(s):  
Feature Extraction ◽  
Process Planning ◽  
Planning System ◽  
Manufacturing Cost ◽  
Design Data ◽  
Feature Extraction Method ◽  
Automatic Feature Extraction ◽  
Starting Point ◽  
Planning Function ◽  
Optimal Approach

Abstract Computers are being used increasingly in the process planning function. The starting point of this function involves interpreting design data from a CAD model of the designed component Feature-based technology is becoming an important tool for this. Automatic recognition of features and extraction of feature information from CAD data can be used to drive a process planning system. In this paper a new approach to automatic feature extraction called the Blank-Surface Concave-edge (BS-CE) approach is illustrated. This approach attempts to remove as much of the blank material with a given machine setup as possible. Hence intuitively one can say that the manufacturing cost of material removal may be minimized if this technique is employed. This feature extraction method is explained along with examples of its implementation. An analysis of alternate feature extraction results is performed and the cost of manufacture is compared to demonstrate the near optimal performance of this technique.

scholarly journals Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3930 ◽  
Author(s):  
Jaehyun Yoo ◽  
Jongho Park
Keyword(s):  
Feature Extraction ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Training Data ◽  
Data Sets ◽  
Feature Extraction Method ◽  
Fingerprinting Method ◽  
Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

Tool Breakage Feature Extraction in PCB Micro-Hole Drilling Using Vibration Signals

2012 ◽  
Vol 497 ◽  
pp. 126-131 ◽  
Author(s):  
Zhen Hua Ren ◽  
Xiao Hu Zheng ◽  
Qing Long An ◽  
Cheng Yong Wang ◽  
Ming Chen
Keyword(s):  
Feature Extraction ◽  
Printed Circuit Board ◽  
Vibration Signal ◽  
Circuit Board ◽  
Hole Drilling ◽  
Features Selection ◽  
Tool Breakage ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Micro Hole

Tool breakage monitoring is crucial to automation fabrication, especially for high-density hole machining, such as PCB (Printed Circuit Board). A tool breakage feature extraction method in PCB micro-hole drilling is presented in this paper. The vibration signal is analyzed by wavelet transform. The decomposed signals energy ratio at each frequency band is computed as monitoring features. The monitoring performance of different features selection is given. The vibration signals are observed to provide the capability in distinguishing micro drill breakage with proper features extraction and classifier design.

Effect of information gain on document classification using k-nearest neighbor

2022 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Rifki Indra Perwira ◽  
Bambang Yuwono ◽  
Risya Ines Putri Siswoyo ◽  
Febri Liantoni ◽  
Hidayatulah Himawan
Keyword(s):  
Feature Selection ◽  
Test Data ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Information Gain ◽  
Training Data ◽  
State Universities ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Support Students

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.

scholarly journals Classification of Sambas Traditional Fabric “Kain Lunggi” Using Texture Feature

2019 ◽  
Vol 13 (4) ◽  
pp. 389
Author(s):  
Alda Cendekia Siregar ◽  
Barry Ceasar Octariadi
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Classification Accuracy ◽  
Selection Process ◽  
Image Features ◽  
K Value ◽  
Feature Extraction Method

Traditional fabric is a cultural heritage that has to be preserved. Kain Lunggi is Sambas traditional fabric that saw a decline in its crafter. To introduce Kain Lunggi in a broader national and global society in order to preserve it, a digital image processing based system to perform Kain Lunggi pattern recognition need to be built. Feature extraction is an important part of digital image processing. The visual feature that does not represent the character of an object will affect the accuracy of a recognition system. The purposes of this research are to perform feature selection on sets of feature to determine the best feature that can increase recognition accuracy. This research conducted in several steps which are image acquisition of Kain Lunggi pattern, preprocessing to reduce image noise, feature extraction to obtain image features, and feature selection. GLCM is implemented as a feature extraction method.  Feature extraction result will be used in a feature selection process using CFS (Correlation-based Feature Selection) methods. Selected features from CFS process are Angular Second Moment, Contrast, and Correlation. Selected features evaluation is conducted by calculating classification accuracy with the KNN method. Classification accuracy prior to feature extraction is 85.18% with K values K=1 ; meanwhile, the accuracy increases to 88.89% after feature selection. The highest accuracy improvement of 20.74% in KNN occurred when using K value K= 4.

scholarly journals A Chronological Method of Detecting Image based Email

2019 ◽  
Vol 8 (2) ◽  
pp. 4579-4583
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Automatic Segmentation ◽  
Quick Response ◽  
Visual Feature ◽  
Feature Extraction Method ◽  
Knn Classifier ◽  
Minimum Number ◽  
Visual Feature Extraction ◽  
Set Up

In this paper we present a Visual feature extraction using improvised SVM and KNN classifiers. The proposed method is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The KNN classifier is used to extract features by predicting nearest neighbour while SVM, analyze the data for classification and regression. The hybrid-based Visual feature extraction and classification is elaborated wherein this work discuss the proposed approach which incorporated using improvised SVM and KNN classifier. Moreover, identified patterns via feature extraction method by means of a minimum number of features that are effective in discriminating pattern classes. With all the aforementioned concepts elaborated, the experimental set-up was elaborated with the experimental task, and the results of the character recognition component are further elucidated.

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