Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Information Sciences: an International Journal
A fuzzy set-based accuracy assessment of soft classification
Pattern Recognition Letters
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Artificial Intelligence in Medicine
Extracting gene regulation information for cancer classification
Pattern Recognition
Data-driven decomposition for multi-class classification
Pattern Recognition
Classification method using fuzzy level set subgrouping
Expert Systems with Applications: An International Journal
Constructing the gene regulation-level representation of microarray data for cancer classification
Journal of Biomedical Informatics
Temporal gene expression classification with regularised neural network
International Journal of Bioinformatics Research and Applications
Artificial Intelligence in Medicine
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
Brief Communication: Finding rule groups to classify high dimensional gene expression datasets
Computational Biology and Chemistry
A novel approach to neuro-fuzzy classification
Neural Networks
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
Expert Systems with Applications: An International Journal
New gene selection method for multiclass tumor classification by class centroid
Journal of Biomedical Informatics
Computational Statistics & Data Analysis
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach
Computational Biology and Chemistry
Computers and Operations Research
Computers in Biology and Medicine
Bayesian classification for bivariate normal gene expression
Computational Statistics & Data Analysis
Computers in Biology and Medicine
Analysis of gene microarray data in a soft computing framework
Applied Soft Computing
A semi-supervised fuzzy clustering algorithm applied to gene expression data
Pattern Recognition
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
DSP-based hierarchical neural network modulation signal classification
IEEE Transactions on Neural Networks
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
A Bayesian stochastic search method for discovering Markov boundaries
Knowledge-Based Systems
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
Spatial interaction - modification model and applications to geo-demographic analysis
Knowledge-Based Systems
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Classification is an important data mining task that widely used in several different real world applications. In microarray analysis, classification techniques are applied in order to discriminate diseases or to predict outcomes based on gene expression patterns, and perhaps even to identify the best treatment for given genetic signature. The most important challenge in gene expression data analysis lies in how to deal with its unique ''high dimension small sample'' characteristic, which makes many traditional classification techniques non-applicable or inefficient; and hence, more dedicated techniques are nowadays needed in order to approach this problem. Fuzzy logic is recently shown that is a powerful and suitable soft computing tool for handling the complex problems under incomplete data conditions. In this paper, a new hybrid model is proposed that combines artificial intelligence with fuzzy in order to benefit from unique advantages of both fuzzy logic and the classification power of the artificial neural networks (ANNs), to construct an efficient and accurate hybrid classifier in less available data situations. The proposed model, because of using the fuzzy parameters instead of the crisp parameters, will need less data set in comparing with traditional nonfuzzy neural networks in its training process or with same training sample can better learn and hence can yield more accurate results than traditional neural networks. In addition of theoretical evidence of using fuzzy logic, empirical results of gene expression classification indicate that the proposed model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks (ANNs) and also some other well-known statistical and intelligent classification models such as the linear discriminant analysis (LDA), the quadratic discriminant analysis (QDA), the K-nearest neighbor (KNN), and the support vector machines (SVMs). Therefore, the proposed model can be applied as an appropriate alternate approach for solving problems with scant data such as gene expression data classification, specifically when higher classification accuracy is needed.