Search Results for author:
Found 15 papers, 4 papers with code
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics
Here we apply the Maximum Overlap Discrete Wavelet Transform to both reduce signal \textit{noise} and use signal variance exhibited at differing inherent frequency levels to develop various metrics of connection between the electrodes placed upon the scalp.
Enhancing Cluster Quality of Numerical Datasets with Domain Ontology
Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters.
Signal Classification using Smooth Coefficients of Multiple wavelets
Classification of time series signals has become an important construct and has many practical applications.
A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization
Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain.
Adaptive Decision Forest: An Incremental Machine Learning Framework
Moreover, ADF is capable of handling big data if the data can be divided into batches.
Detecting Autism Spectrum Disorder using Machine Learning
Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch.
FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy
Random Forest remains one of Data Mining's most enduring ensemble algorithms, achieving well-documented levels of accuracy and processing speed, as well as regularly appearing in new research.
A Novel Incremental Clustering Technique with Concept Drift Detection
Therefore, incremental clustering techniques need the ability to detect a temporary drift and sustained drift.
Tree Index: A New Cluster Evaluation Technique
Our Tree Index produces a decision tree from the clustered data set, using the cluster identifiers as labels.
Data Pre-Processing and Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement
We then apply and compare the effectiveness of different data mining methods namely decision tree (DT), artificial neural networks (ANNs), systematically developed forest (SysFor) for multiple trees, support vector machine (SVM), logistic regression, and the traditional Evapotranspiration (ETc) methods and evaluate the performance of these models to predict irrigation water demand.
DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data-mining and knowledge discovery tools for mobile devices are surprisingly rare.
Decision Tree Classification with Differential Privacy: A Survey
Data mining information about people is becoming increasingly important in the data-driven society of the 21st century.
Differentially Private Random Decision Forests using Smooth Sensitivity
We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries.
Cryptography and Security
Measuring pattern retention in anonymized data -- where one measure is not enough
This question is not the same as asking if an accurate classifier can be built from the modified data.
