Md. Eusha Kadir

An Evidential Dynamic Neighborhood Approach to Learn from Imbalanced Data

Md. Eusha Kadir, Md. Mumtahin Habib Ullah Mazumder, Muhammad Alam, Mohammad Shoyaib

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An overlapping conscious relief-based feature subset selection method

Nishat Tasnim Mim, Md. Eusha Kadir, Suravi Akhter, M.A. Khan

Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based …

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cFEM: a cluster based feature extraction method for network intrusion detection

Md. Mumtahin Habib Ullah Mazumder, Md. Eusha Kadir, Sadia Sharmin, Md. Shariful Islam et autres

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Feature Subset Selection based on Redundancy Maximized Clusters

Md. Hasan Tarek, Md. Eusha Kadir, Sadia Sharmin, Abu Ashfaqur Sajib et autres

Feature selection plays a vital role in the field of data mining and machine learning for analyzing high-dimensional data. A popular criteria for feature selection is Mutual Information (MI) as it can capture both the linear and non-linear relationship among different features …

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Mutual Information based Feature Selection for Nurse Care Activity Recognition

Md. Hasan Tarek, Md. Eusha Kadir, Mahir Mahbub, Pritom Saha Akash et autres

Human activity recognition is a challenging task as performing the activities varies from person to person. For the last few years, many complex methods have been proposed to identify human activities from sensor readings. To date, several studies have been conducted successfully …

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A Proximity Weighted Evidential k Nearest Neighbor Classifier for Imbalanced Data

Md. Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali et autres

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An Ensemble Approach to Detect Code Comment Inconsistencies using Topic Modeling.

Fazle Rabbi, Nazmul Haque, Md. Eusha Kadir, Md. Saeed Siddik et autres

Can a simple approach identify complex nurse care activity?

Md. Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali et autres

For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple …

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Inter-node Hellinger Distance based Decision Tree

Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. …

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Evidential SVM for binary classification

Md. Eusha Kadir, Pritom Saha Akash, Amin Ahsan Ali, Mohammad Shoyaib et autres

Support Vector Machine (SVM) is one of the most popular supervised learning methods for its better performances over diversified applications. SVM constructs a maximum-margin hyperplane and predicts the class of a new incoming data point based on that hyperplane. However, the hyperplane …

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Introducing Confidence as a Weight in Random Forest

Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Md. Nurul Ahad Tawhid et autres

Random forest is one of the most popular supervised learning methods which is a collection of multiple decision trees. It is computationally fast, easy to use and gives reasonable performances over diversified applications. In a random forest, outcomes from multiple trees are …

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