Multilayer feedforward networks are universal approximators
Neural Networks
The Strength of Weak Learnability
Machine Learning
Digital image processing
Machine Learning
Averaging regularized estimators
Neural Computation
Ensemble learning via negative correlation
Neural Networks
FANNC: a fast adaptive neural network classifier
Knowledge and Information Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Computer Aided Diagnosis System for Lung Cancer Based on Helical CT Images
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Classification of seismic signals by integrating ensembles ofneural networks
IEEE Transactions on Signal Processing
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
Ensembling neural networks: many could be better than all
Artificial Intelligence
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
An introduction to boosting and leveraging
Advanced lectures on machine learning
The responsibility gap: Ascribing responsibility for the actions of learning automata
Ethics and Information Technology
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Learning the Topological Properties of Brain Tumors
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Extracting symbolic rules from trained neural network ensembles
AI Communications - Artificial Intelligence Advances in China
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Quick and reliable diagnosis of stomach cancer by artificial neural network
MCBC'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry
A data driven ensemble classifier for credit scoring analysis
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Predicting the Occupancy of the HF Amateur Service with Neural Network Ensembles
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
ECM-aware cell-graph mining for bone tissue modeling and classification
Data Mining and Knowledge Discovery
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
An ensemble method for medicine best selling prediction
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Quick and reliable diagnosis of stomach cancer by artificial neural network
BEBI'09 Proceedings of the 2nd WSEAS international conference on Biomedical electronics and biomedical informatics
An expert system for improving web-based problem-solving ability of students
Expert Systems with Applications: An International Journal
Municipal revenue prediction by ensembles of neural networks and support vector machines
WSEAS Transactions on Computers
Classification of pulmonary nodules using neural network ensemble
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Lung cancer detection using labeled sputum sample: multi spectrum approach
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
An intelligent model for the classification of children's occupational therapy problems
Expert Systems with Applications: An International Journal
Analyzing domain expertise by considering variants of knowledge in multiple time scales
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Spiculated lesion detection in digital mammogram based on artificial neural network ensemble
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
The build of n-Bits Binary Coding ICBP Ensemble System
Neurocomputing
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Computers in Biology and Medicine
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Towards effective algorithms for intelligent defense systems
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Cell-graph coloring for cancerous tissue modelling and classification
Multimedia Tools and Applications
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
Automated trading with performance weighted random forests and seasonality
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.