arXiv subjects

Chanabasayya M. Vastrad

Publications and source records attributed to Chanabasayya M. Vastrad.

3 recordsLinked to original sources

arXiv2014

Non linear Prediction of Antitubercular Activity Of Oxazolines and Oxazoles derivatives Making Use of Compact TS-Fuzzy models Through Clustering with orthogonal least sqaure technique and Fuzzy identification system

The prediction of uncertain and predictive nonlinear systems is an important and challenging problem. Fuzzy logic models are often a good choice to describe such systems however in many cases these become complex soon. commonlly, too less effort is put into descriptor selection and in the creation of suitable local rules. Moreover, in common no model reduction is applied, while this may analyze the model by removing redundant data. This paper suggests a combined method that deal with these issues in order to create compact Takagi Sugeno (TS) models that can be effectively used to represent complex predictive systems. A new fuzzy clustering method is come up with for the identification of compact TS-fuzzy models. The best relevant consequent variables of the TS model are choosen by an orthogonal least squares technique based on the obtained clusters.For the selection of the relevant antecedent (scheduling) variables a new method has been developed based on Fisher's interclass separability basis. This complete approach is demonstrated by means of the Oxazolines and Oxazoles derivatives as antituberculosis agent for nonlinear regression benchmark. The results are compared with results obtained by neuro-fuzzy i.e. ANFIS algorithm and advanced fuzzyy clustering techniques i.e FMID toolbox .

cs.CE↗

arXiv2014

Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity

In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model such as partial least squares(PLS). In this study, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each descriptor Then, based on the importance level of each descriptor, STRS sequentially selects N subsets of descriptors from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a regresson model. Next, based on the regression coefficients, a two-step procedure including rapidly decreasing function (RDF) based enforced descriptor selection and self tuned sampling (STS) based competitive descriptor selection is adopted to select the important descriptorss. After running the loops, a number of subsets of descriptors are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of descriptors is computed. The subset of descriptors with the lowest RMSECV is considered as the optimal descriptor subset. The performance of the proposed algorithm is evaluated by sulfanomide derivative dataset. The results reveal an good characteristic of STRS that it can usually locate an optimal combination of some important descriptors which are interpretable to the biologically of interest. Additionally, our study shows that better prediction is obtained by STRS when compared to full descriptor set PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).

cs.LG↗

arXiv2013

Study Of E-Smooth Support Vector Regression And Comparison With E- Support Vector Regression And Potential Support Vector Machines For Prediction For The Antitubercular Activity Of Oxazolines And Oxazoles Derivatives

A new smoothing method for solving ? -support vector regression (?-SVR), tolerating a small error in fitting a given data sets nonlinearly is proposed in this study. Which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with a ?-insensitive support vector regression. We term this redeveloped problem as ?-smooth support vector regression (?-SSVR). The performance and predictive ability of ?-SSVR are investigated and compared with other methods such as LIBSVM (?-SVR) and P-SVM methods. In the present study, two Oxazolines and Oxazoles molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of experiments. Primary experimental results illustrate that our proposed approach improves the regression performance and the learning efficiency. In both studied cases, the predictive ability of the ?- SSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate that ?-SSVR can be used as an alternative powerful modeling method for regression studies. The experimental results show that the presented algorithm ?-SSVR, plays better precisely and effectively than LIBSVMand P-SVM in predicting antitubercular activity.

cs.CE↗