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Multiclass Least Squares Twin Support Vector Machine for Pattern Classification
Divya Tomar,Sonali Agarwal 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one for each class, by solving M-linear equations. A regularization term is also added to improve the generalization ability. MLSTSVM works well for both linear and non-linear type of datasets. It is relatively simple and fast algorithm as compared to the other existing approaches. The performance of proposed approach has been evaluated on twelve benchmark datasets. The experimental result demonstrates the validity of proposed MLSTSVM classifier as compared to the typical multi-classifiers based on ‘Support Vector Machine’ and ‘Twin Support Vector Machine’. Statistical analysis of the proposed classifier with existing classifiers is also performed by using Friedman’s Test statistic and Nemenyi post hoc techniques.
Link Prediction for Authorship Association in Heterogeneous Network Using Streaming Classification
Harshal Singh,Divya Tomar,Sonali Agarwal 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.4
Prediction of links or relations between the objects in any network is no longer a new task these days; in fact it has become a high rated area of research and has attracted many researchers seeking their contribution to the mentioned area. Research has seen an exponential growth over the passing years, and the active researchers do not hesitate in linking with fellow researchers working in same domain irrespective of their geographic location. However this in turn has generated a very complex network of objects and links which are needed to be analyzed and dealt with. Prediction of co-authorship is the sub domain of link prediction and with the increasing complexity of co-authorship network the authors are treated as heterogeneous entity not as homogeneous ones. The rule is simple analyze the data preprocess it, train the classifier according to desired classification rules and then get the classified form of data. But irrelevant features always reflect various impacts and issues on generation of a classifier and consequently the impact is sustained to further classification results. Therefore, this paper proposes streaming classification algorithm combined with Correlation based Feature selection as a solution to the stated problem. The consistent and relevant features are selected with the help of feature selection algorithm and then these features are classified with the help of streaming classification algorithm- Very Fast Decision Tree (VFDT). VFDT is a streaming classification algorithm and it takes the dataset in the form of continuous stream as an input. Finally the effectiveness of the proposed algorithm can be seen in the experimental results.
Positioning errors and quality assessment in panoramic radiography
Manu Dhillon,Srinivasa M Raju,Sankalp Verma,Divya tomar,Raviprakash S Mohan,Manisha Lakhanpal,Bhuvana Krishnamoorthy 대한구강악안면방사선학회 2012 Imaging Science in Dentistry Vol.42 No.4
Purpose: This study was performed to determine the relative frequency of positioning errors, to identify those errors directly responsible for diagnostically inadequate images, and to assess the quality of panoramic radiographs in a sample of records collected from a dental college. Materials and Methods: This study consisted of 1,782 panoramic radiographs obtained from the Department of Oral and Maxillofacial Radiology. The positioning errors of the radiographs were assessed and categorized into nine groups: the chin tipped high, chin tipped low, a slumped position, the patient positioned forward, the patient positioned backward, failure to position the tongue against the palate, patient movement during exposure, the head tilted, and the head turned to one side. The quality of the radiographs was further judged as being ‘excellent’, ‘diagnostically acceptable’, or ‘unacceptable’. Results: Out of 1,782 radiographs, 196 (11%) were error free and 1,586 (89%) were present with positioning errors. The most common error observed was the failure to position the tongue against the palate (55.7%) and the least commonly experienced error was patient movement during exposure (1.6%). Only 11% of the radiographs were excellent, 64.1% were diagnostically acceptable, and 24.9% were unacceptable. Conclusion: The positioning errors found on panoramic radiographs were relatively common in our study. The quality of panoramic radiographs could be improved by careful attention to patient positioning.
An Ensemble Approach for Efficient Churn Prediction in Telecom Industry
Pretam Jayaswal,Bakshi Rohit Prasad,Divya Tomar,Sonali Agarwal 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8
The rise of globalization and market liberalization are changing the face of market competitiveness significantly. The appearance of modern technology in business processes has intensified the competition and put forth new challenges for service providing companies. To cope up with changing scenarios, companies are shifting their attention on retaining the existing customers rather hiring new ones. This is more cost effective and requires lesser resource as well. The phenomenon of abandoning the company by a customer is known as churn and in this context, anticipating the customer's intention to churn is called churn prediction. Data Mining and machine learning techniques, as applied to customer behavior and usage information, can assist the churn management processes. This paper used customer usage and related information from a telecom service provider to analyze churn in telecom industry. The decision trees and its ensembles, Random Forest and Gradient Boosted trees are used as underlying statistical machine learning models for building the binary churn classifier. The implementation part has been done using apache spark which is state of the art unified data analysis framework for machine learning and data mining. In order to achieve better and efficient results, the grid based hyper-parameter optimization is applied.
Positioning errors and quality assessment in panoramic radiography
Dhillon, Manu,Raju, Srinivasa M.,Verma, Sankalp,Tomar, Divya,Mohan, Raviprakash S.,Lakhanpal, Manisha,Krishnamoorthy, Bhuvana Korean Academy of Oral and Maxillofacial Radiology 2012 Imaging Science in Dentistry Vol.42 No.4
Purpose: This study was performed to determine the relative frequency of positioning errors, to identify those errors directly responsible for diagnostically inadequate images, and to assess the quality of panoramic radiographs in a sample of records collected from a dental college. Materials and Methods: This study consisted of 1,782 panoramic radiographs obtained from the Department of Oral and Maxillofacial Radiology. The positioning errors of the radiographs were assessed and categorized into nine groups: the chin tipped high, chin tipped low, a slumped position, the patient positioned forward, the patient positioned backward, failure to position the tongue against the palate, patient movement during exposure, the head tilted, and the head turned to one side. The quality of the radiographs was further judged as being 'excellent', 'diagnostically acceptable', or 'unacceptable'. Results: Out of 1,782 radiographs, 196 (11%) were error free and 1,586 (89%) were present with positioning errors. The most common error observed was the failure to position the tongue against the palate (55.7%) and the least commonly experienced error was patient movement during exposure (1.6%). Only 11% of the radiographs were excellent, 64.1% were diagnostically acceptable, and 24.9% were unacceptable. Conclusion: The positioning errors found on panoramic radiographs were relatively common in our study. The quality of panoramic radiographs could be improved by careful attention to patient positioning.