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Currently, various perspectives of neural networks are proposed for solving classification problems. Some of them are based on two types of mapping functions, namely, linear and nonlinear, for mapping an input space into a feature space. In addition, some neural networks are proposed based on probability theory. Since some models are appropriated for some kinds of data, depending on a distribution...
To employ and develop the performance of the dimensionality reduction for microarray data there is need of good dimension reduction technique. High-dimensional data bring great challenges in terms of computational complexity and classification performance. Therefore, it is necessary to effectively compress in a low-dimensional feature space from high dimensional feature space to design a learner with...
Research on feature selection techniques for identifying informative genes from high dimensional microarray datasets has received considerable attention. Numerous researchers have proposed various optimized solutions to reduce noises, redundancy in dataset and to enhance the accuracy and generalization of the classification model by applying many computational tools. High-dimensional microarray gene...
Primary tumor is a neoplasm which in clinical parlance is regarded as malignant, arising in one site and capable of giving rise to metastatic tumors. Primary tumor disease is a major health problem in today's time. This paper aims at analyzing various data mining techniques for primary tumor prediction. The observations reveal that the hybrid approach of any three classifiers using Vote ensemble technique...
The development of data mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. Cancer classification has improved over the past 20 years; there has been no general approach for identifying new cancer classes or for assigning tumors to known classes (class prediction). Most proposed cancer classification methods...
CT imaging shows that it is approximately symmetrical about the perpendicular bisector. Based on this medical knowledge guidance, symmetry theory based classification algorithm in CT image database is presented in this paper. First of all, the definitions of the weak symmetry and strong symmetry were given. Then, the weak symmetry was applied to the first stage classification of the CT images. Secondly,...
The DNA microarray classification is one of the most popular technique among researchers and practitioners. In microarray data analysis, huge useful information may be lost due to irrelevant and insignificant features of the dataset. To overcome this drawback of the data set, only those features are selected which have high relevance with the classes and high significance in the feature set. In this...
This paper presents a method for designing binary trees for SVM classification. The proposed algorithm, multi-modal binary tree (MBT) tolerates misclassification in the upper nodes of the tree, allowing points to be classified in either output regardless of the initial specified class groupings. MBT can separate classes that are inseparable with a single classifier by using a piecewise division. The...
The aim of our work was to design and implement a software solution, which supports quantitative histological analysis of hematoxilin eozin (HE) stained colon tissue samples, identify tissue structures - nuclei, glands and epithelium - using image processing methods. Furthermore, based on the result of the histological segmentation, it gives a suggestion for the negative or malignant status of the...
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels...
Currently classifying high-dimensional data is a very challenging problem. High dimensional feature spaces affect both accuracy and efficiency of supervised learning methods. To address this issue, we present a fast and efficient feature selection algorithm to facilitate classifying high-dimensional datasets as those appearing in Bioinformatics problems. Our method employs a Laplacian score ranking...
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. But microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. This paper proposes the multiclass Flexible Neural Tree (FNT) algorithm for cancer classification. Based on the pre-defined instruction/operator...
Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes...
Training artificial neural networks (ANNs) is a complex task of great importance in problems of supervised learning. Evolutionary algorithms (EAs) are widely used as global searching techniques for optimization in scientific and engineering problems, and these approaches have been introduced to ANNs to perform various tasks, such as connection weight training and architecture design. Recently, a novel...
Methods currently used for micro-array data classification aim to select a minimum subset of features, namely a predictor, that is necessary to construct a classifier of best accuracy. Although effective, they lack in facing the primary goal of domain experts that are interested in detecting different groups of biologically relevant markers. In this paper, we present and test a framework which aims...
Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, rule based system can produce interpretable classifier with knowledge compressed in terms of rules. This paper proposes a rule based approach called "Large coverage rule (LCR)" for microarray data...
In recent years, cancer can be detected and recognized by analyzing the sample's expression profile. The cancer gene expression data are high dimensional, high variable dependent, and very noisy. The dimension reduction method is often used for processing the high dimensional data. In this study, a new statistical dimension reduction method called Expressive Value Distance (EVD) is developed and proposed...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
Support vector machine is a widely used tool in the field of image processing and pattern recognition. The parameters selection plays a significant role in support vector machine(SVM). This paper proposed an improved parameter optimization method based on traditional PSO optimizing algorithm by changing the fitness function in the traditional process. And this method has achieved better results which...
We investigate classification algorithms LDA, SPRT and a modified SPRT on clinical datasets for Parkinson's disease, colon cancer, and breast cancer. The SPRT algorithms were run with components in decreasing variance order and random order. Results for those in random order were calculated as the majority predictions over 100 runs. Truncation was always set to the total number of components of the...
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