Results 261 to 270 of about 75,083 (296)

A Hybrid Higher Order Neural Classifier for handling classification problems

Expert Systems with Applications, 2011
In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of
Mehdi Fallahnezhad, Mohammad Hassan Moradi, Salman Zaferanlouei   +2 more
openaire   +1 more source

Boosting Lite – Handling Larger Datasets and Slower Base Classifiers

2007
In this paper, we examine ensemble algorithms (Boosting Lite and Ivoting) that provide accuracy approximating a single classifier, but which require significantly fewer training examples. Such algorithms allow ensemble methods to operate on very large data sets or use very slow learning algorithms.
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer   +3 more
openaire   +1 more source

New evolving ensemble classifier for handling concept drifting data streams

2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, 2012
Data streams mining have become a novel research topic of growing interest in knowledge discovery. The data streams which are generated from applications, such as network analysis, real time surveillance systems, sensor networks and financial generate huge data streams. These data streams consist of millions or billions of updates and must be processed
Kapil Wankhade, Snehlata Dongre, Ravindra Thool   +2 more
openaire   +1 more source

Handling Different Levels of Granularity within Naive Bayes Classifiers

2013
Data mining techniques usually require a flat data table as input. For categorical attributes, there is often no canonical flat data table, since they can often be considered in different levels of granularity like continent, country or local region. The choice of the best level of granularity for a data mining task can be very tedious, especially when
Kemal Ince, Frank Klawonn
openaire   +1 more source

Pedestrian Detection for Autonomous Cars: Occlusion Handling by Classifying Body Parts

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
In this work, we address the problem of detecting body parts of pedestrians using deep neural networks. In particular, we consider the occluded pedestrian detection problem in autonomous driving settings. While state-of-the-art deep neural models perform reasonably well for detecting full-body pedestrians, their performances are not satisfactory for ...
Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Balakrishna Gokaraju, Ali Karimoddini   +3 more
openaire   +1 more source

Weak Classifiers Performance Measure in Handling Noisy Clinical Trial Data

2016
Most research concluded that machine learning performance is better when dealing with cleaned dataset compared to dirty dataset. In this paper, we experimented three weak or base machine learning classifiers: Decision Table, Naive Bayes and k-Nearest Neighbor to see their performance on real-world, noisy and messy clinical trial dataset rather than ...
Ezzatul Akmal Kamaru-Zaman, Andrew Brass, James Weatherall, Shuzlina Abdul Rahman   +3 more
openaire   +1 more source

Efficient handling of high-dimensional feature spaces by randomized classifier ensembles

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02, 2002
Handling massive datasets is a difficult problem not only due to prohibitively large numbers of entries but in some cases also due to the very high dimensionality of the data. Often, severe feature selection is performed to limit the number of attributes to a manageable size, which unfortunately can lead to a loss of useful information.
Aleksander Kołcz, Xiaomei Sun, Jugal Kalita   +2 more
openaire   +1 more source

Handling Varying Amounts of Missing Data when Classifying Mental-Health Risk Levels

2014
One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large
Sherine Nagy, Saleh, Christopher D, Buckingham   +1 more
openaire   +2 more sources

Classifying Complaint Reports Using RNN and Handling Imbalanced Dataset

2022 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2022
Oktefvia Aruda Lisjana, Masayu Leylia Khodra   +1 more
openaire   +1 more source

Review of Combat Ammunition System (CAS) Classified Data Handling.

1996
Abstract : This research study examined the Combat Ammunition System, Base-level (CAS-B). Specifically it addresses issues of the multi-level secure (MLS) designation of the system and the data within the system that requires it to be classified. The issue of classification is traced from development of the system, when it was intended to store data ...
openaire   +1 more source