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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.
Second Order Nonsmooth Multiobjective Fractional Programming Problem Involving Support Functions
Pallavi Kharbanda,Divya Agarwal,Deepa Sinha 한국전산응용수학회 2013 Journal of applied mathematics & informatics Vol.31 No.5
In this paper, we have considered a class of constrained nonsmoothmultiobjective fractional programming problem involving supportfunctions under generalized convexity. Also, second order Mond Weir typedual and Schaible type dual are discussed and various weak, strong andstrict converse duality results are derived under generalized class of secondorder (F, α, ρ, d)-V-type I functions. Also, we have illustrated through nontrivialexamples that class of second order (F, α, ρ, d)-V-type I functionsextends the definitions of generalized convexity appeared in the literature.
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.
SECOND ORDER NONSMOOTH MULTIOBJECTIVE FRACTIONAL PROGRAMMING PROBLEM INVOLVING SUPPORT FUNCTIONS
Kharbanda, Pallavi,Agarwal, Divya,Sinha, Deepa The Korean Society for Computational and Applied M 2013 Journal of applied mathematics & informatics Vol.31 No.5
In this paper, we have considered a class of constrained non-smooth multiobjective fractional programming problem involving support functions under generalized convexity. Also, second order Mond Weir type dual and Schaible type dual are discussed and various weak, strong and strict converse duality results are derived under generalized class of second order (F, ${\alpha}$, ${\rho}$, $d$)-V-type I functions. Also, we have illustrated through non-trivial examples that class of second order (F, ${\alpha}$, ${\rho}$, $d$)-V-type I functions extends the definitions of generalized convexity appeared in the literature.
Clinical and Social Outcomes of Cochlear Implantation in Older Prelinguals
Tyagi Pragya,Chauhan Divya,Singh Anup,Bhutada Mayank,Sikka Kapil,Chaudhary Tanvi,Sharma Sonam,Agarwal Shivani,Verma Hitesh,Sagar Prem,Kumar Rakesh,Thakar Alok 대한청각학회 2023 Journal of Audiology & Otology Vol.27 No.2
Background and Objectives: Cochlear implantation in late implanted prelinguals necessitates a complex decision-making process for clinicians and patients due to the uncertainty of achieving adequate benefit in auditory and speech perception. This study longitudinally evaluated clinical and social outcomes of prelingually deaf children with implantation in their late childhood.Subjects and Methods: A total of 113 (49 females and 64 males) participants, with an age range of 5-15 years, were assessed for the pre-implant parameters such as hearing loss etiology, aided responses, anatomical aspects, and psychological evaluation. The Category of Auditory Performance, Speech Awareness Threshold, Speech Reception Threshold, and Speech Discrimination Score were administered to assess the patient’s auditory skills. Further, the Speech Intelligibility Rating scale was administered to evaluate the patient’s speech intelligibility at 3, 6, 9, 12, 18, and 24 months post-surgery. Subjectively perceived benefits were evaluated using the satisfaction rating scale and a questionnaire.Results: The statistical results showed a significant impact of cochlear implantation in all domains. Positive impact and improvement post-implantation were noted in all the spheres, including auditory, linguistic, social, and educational.Conclusions: The study highlighted that the outcomes of a cochlear implant at a later age might not parallel with the implantation at a younger age. However, this still provides measurable benefits even after a longer period of auditory deprivation.
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.