Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Kamath S․, S.Subject: search.filters.subject.Feature selection

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    Novel hybrid feature selection models for unsupervised document categorization
    (Institute of Electrical and Electronics Engineers Inc., 2017) Bhopale, A.P.; Kamath S․, S.
    Dealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection method selects only common features identified by sub-models. However, in reality, any type of feature selection can cause a loss of some potentially important features. In this paper, a hybrid feature selection model called Modified Hybrid Union (MHU) is proposed, which selects features by considering the individual strengths and weaknesses of each constituent component of the model. A comparative evaluation of its performance for K-means clustering and Bio-inspired Flockbased clustering is also presented on standard data sets such as OWL-S TC and Reuters-21578. © 2017 IEEE.
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    Detection of Cardiac Arrhythmia Using Machine Learning Approaches
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chittoria, J.; Kamath S․, S.; Mayya, V.
    Arrhythmia is a cardiovascular disease that alters the heart rate, resulting in too fast, too slow, or irregular rhythms. It is a life-threatening disease if left untreated. Traditionally, arrhythmia is diagnosed by a trained doctor, using an electrocardiogram to analyze irregular heartbeats. However, these methods are vulnerable to inadvertent misdiagnosis, especially during the early stages of the disease. In this paper, an approach for cardiac arrhythmia detection is presented, where the subjects or instances are first categorized as diseased or normal and then further graded into normal (non-diseased) or as distinct subtypes of cardiac arrhythmia. The dataset was obtained from the UCI Machine Learning Data Repository, and machine learning methods such as XGBoost, CatBoost, SVM, and Random Forest, were experimented with. Addition-ally, the mutual information-based feature selection approach, minimal redundancy maximum relevance (mRMR), is proposed to improve classification accuracy. Standard evaluation metrics such as accuracy, f1-score, precision, and recall are utilized for comparison of the obtained results. The experimental results demonstrated that accuracy of 81.48% was achieved for multi-class classification, while binary classification achieved up to 84% accuracy. © 2022 IEEE.