Browsing by Author "Nickolas, S"

Now showing 1 - 4 of 4
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    A self-learning algorithm for identifying the leverage points in soil data using quantile regression forests
    (Elsevier Inc., 2023-12-14) Rose, Sareena; Nickolas, S; Sunoj, S.M.; Sangeetha, S
    Some unusual combinations of predictor values in multivariate regression often influence tampering with the output, and filtering those observations becomes the trickiest and most challenging task. This concern is prevalent and predominant in ecological domains, especially in soil samples, as the data sets are heteroscedastic and heterogeneous. When there is little domain knowledge on the combinatorial criterion for the leverage points, it is advantageous to derive a labelled framework to differentiate the unusual observations. This study proposes a novel framework by integrating quantiles and proximity matrix of Quantile Regression Forest that builds a framework out of the training data set. Unlike other supervised anomalous detection algorithms, prior knowledge about the samples is not required to train the dataset, as the algorithm works in a self-learning mode. The outcome is two sets of observations: regular and leverage points. When unseen data arrives, the regressors’ proximity to these two observation sets is the demarcation criterion. Three real datasets are used, and the outcome of the proposed approach is verified using Principal Component Analysis, Local Outlier Factor, and Gaussian Mixture Models. The algorithm's results are promising, setting a new trend of using supervised techniques without demanding any prior knowledge of the observations and performing an inlier-based outlier detection technique.
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    Efficient Pre-Processing Techniques for Improving Classifiers Performance
    (River Publishers, 2021-12-30) Nickolas, S; Shobha, K
    Data pre-processing plays a vital role in the life cycle of data mining for accomplishing quality outcomes. In this paper, it is experimentally shown the importance of data pre-processing to achieve highly accurate classifier outcomes by imputing missing values using a novel imputation method, CLUSTPRO, by selecting highly correlated features using Correlation-based Variable Selection (CVS) and by handling imbalanced data using Synthetic Minority Over-sampling Technique (SMOTE). The proposed CLUSTPRO method makes use of Random Forest (RF) and Expectation Maximization (EM) algorithms to impute missing. The imputed results are evaluated using standard evaluation metrics. The CLUSTPRO imputation method outperforms existing, state-of-the-art imputation methods. The combined approach of imputation, feature selection, and imbalanced data handling techniques has significantly contributed to attaining an improved classification accuracy (AUC curve) of 40%–50% in comparison with results obtained without any pre-processing.
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    Incremental feature selection method for software defect prediction
    (Blue Eyes Intelligence Engineering and Sciences Publication, 2019-07) Gayatri, N; Nickolas, S; Subbarao, A
    Software defect prediction models are essential for understanding quality attributes relevant for software organization to deliver better software reliability. This paper focuses mainly based on the selection of attributes in the perspective of software quality estimation for incremental database. A new dimensionality reduction method Wilk’s Lambda Average Threshold (WLAT) is presented for selection of optimal features which are used for classifying modules as fault prone or not. This paper uses software metrics and defect data collected from benchmark data sets. The comparative results confirm that the statistical search algorithm (WLAT) outperforms the other relevant feature selection methods for most classifiers. The main advantage of the proposed WLAT method is: The selected features can be reused when there is increase or decrease in database size, without the need of extracting features afresh. In addition, performances of the defect prediction models either remains unchanged or improved even after eliminating 85% of the software metrics.
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    Machine learning for high risk pregnancies pre-term birth prediction: A retrospective
    (Science Publishing Corporation Inc, 2018-04-20) Ramla, M; Sangeetha, S; Nickolas, S
    Any birth before 28 weeks of gestation is termed as Pre-Term. This has substantial impact in the emotional reactions of mothers. The post-traumatic stress notably in the mother could be of chronic psychological risk. Moreover, it is to be addressed in the global scenario for sustainable development. Predicting stillbirths is still a distant reality. A plethora of works have been carried out and this paper present the summaries and analysis of current research. The primary focus of the paper is to throw light on the challenging issue of Preterm Birth Prediction. Myriad of machine learning techniques are used by various researchers each with its own estimation accuracy and type of ML model.