المنشورات و المؤلفات
Enhancing distance measures is the key to improve the performance of instance-based learning (IBL) and many machine learning (ML) algorithms. The value difference metrics (VDM) and inverted specific-class distance measure (ISCDM) are among the top...
Web Service Composition (WSC) aims to select and aggregate
many web services to generate a work ow. The workflow contains many tasks
and for each task there are many web services to choose from. The challenge
is to select the best combination of...
The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional probability terms. Sometimes these terms are hard to be...
The main aim of this work is to compare Hindu and Arabic digits with respect to a machine’s ability
to recognize them. This comparison is done on the raw representation (images) of the digits and on
their features extracted using two feature...
Combining Instance Weighting and Fine Tuning for Training Naïve Bayesian Classifiers with Scant data
This work addresses the problem of having to train a Naïve Bayesian classifier using limited data. It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning...
In this work, we propose a Selective Fine-Tuning algorithm for Bayesian Networks (SFTBN). The aim is to enhance the accuracy of Bayesian network classifiers by finding better estimations for the probability terms used by the classifiers. The...
Real-world data are usually noisy, causing many machine-learning algorithms to overfit their data. Various Instance Reduction (IR) techniques have been proposed to filter out noisy instances and clean the data. This paper presents Partial Instance...
This work improves on the FTNB algorithm to make it more tolerant of noise. The FTNB algorithm augments the Naïve Bayesian (NB) learning algorithm with a fine tuning stage in an attempt to find better estimations of the probability terms involved....
This work augments the Naïve Bayesian learning algorithm with a second training phase in an attempt to improve its classification accuracy. This is achieved by finding more accurate estimations of the needed probability terms. This approach helps in...
The classification accuracy of many machine learning methods depends upon their ability to accurately measure the similarity between different instances. Similarity is measured using a distance metric or measure. In this work, several novel...