An efficient semisupervised feedforward neural network clustering

2014 ◽  
Vol 30 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Roya Asadi ◽  
Mitra Asadi ◽  
Sameem Abdul Kareem
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Weight Vector ◽  
Feedforward Neural Network ◽  
Network Clustering ◽  
Class Label ◽  
Number Of Clusters ◽  
The University ◽  
Standard Weight ◽  
Input Instance

AbstractWe developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superiorFmeasure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.

A dynamic semisupervised feedforward neural network clustering

2016 ◽  
Vol 31 (1) ◽  
pp. 30-54 ◽  
Author(s):  
Roya Asadi ◽  
Sameem Abdul Kareem ◽  
Shokoofeh Asadi ◽  
Mitra Asadi
Keyword(s):  
Neural Network ◽  
Medical Center ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Network Clustering ◽  
Online Data ◽  
Data Set ◽  
Number Of Clusters ◽  
Cancer Data ◽  
The University

AbstractAn efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

An Improved Strategy for Predicting Diagnosis, Survivability, and Recurrence of Breast Cancer

2019 ◽  
Vol 16 (9) ◽  
pp. 3705-3711 ◽  
Author(s):  
Souad Larabi Marie-Sainte ◽  
Tanzila Saba ◽  
Deem Alsaleh ◽  
Mashael Bin Alamir Alotaibi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Recurrence ◽  
Breast Cancer Diagnosis ◽  
Feedforward Neural Network ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Efficient Treatment

Breast Cancer is a common disease among females. Early detection of the Breast Cancer aids in an easier efficient treatment. The application of Machine Learning algorithms can help in the diagnosis of this disease. There are three main problems related to Breast Cancer. The existing works focused only on one problem. In addition, the resulted accuracy still needs improvement. This research paper aims to identify the Breast Cancer diagnosis, predict the recurrence of the disease, and predict the survivability of its patients. This is achieved by using the Feedforward Neural Network (FFN) on the SEER (Surveillance, Epidemiology, and End Results) dataset by using different attributes and preprocessing of data for each problem. The obtained FFN classification accuracy resulted in 99.8% for the Breast Cancer diagnosis, 88.1% for the Breast Cancer recurrence, and 97.3% for the survivability.

Modified single-output Chebyshev-polynomial feedforward neural network aided with subset method for classification of breast cancer

2019 ◽  
Vol 350 ◽  
pp. 128-135 ◽  
Author(s):  
Long Jin ◽  
Zhiguan Huang ◽  
Liangming Chen ◽  
Mei Liu ◽  
Yuhe Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Chebyshev Polynomial ◽  
Feedforward Neural Network ◽  
Single Output

On the Geometry of Feedforward Neural Network Error Surfaces

1993 ◽  
Vol 5 (6) ◽  
pp. 910-927 ◽  
Author(s):  
An Mei Chen ◽  
Haw-minn Lu ◽  
Robert Hecht-Nielsen
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Weight Vector ◽  
Feedforward Neural Network ◽  
Weyl Groups ◽  
Weight Space ◽  
Separate Analysis ◽  
Large Numbers ◽  
Multilayer Perceptron Networks ◽  
Simple Equations

Many feedforward neural network architectures have the property that their overall input-output function is unchanged by certain weight permutations and sign flips. In this paper, the geometric structure of these equioutput weight space transformations is explored for the case of multilayer perceptron networks with tanh activation functions (similar results hold for many other types of neural networks). It is shown that these transformations form an algebraic group isomorphic to a direct product of Weyl groups. Results concerning the root spaces of the Lie algebras associated with these Weyl groups are then used to derive sets of simple equations for minimal sufficient search sets in weight space. These sets, which take the geometric forms of a wedge and a cone, occupy only a minute fraction of the volume of weight space. A separate analysis shows that large numbers of copies of a network performance function optimum weight vector are created by the action of the equioutput transformation group and that these copies all lie on the same sphere. Some implications of these results for learning are discussed.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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