Aruna Tiwari

Breast cancer diagnosis using Genetically Optimized Neural Network model

Abstract One in every eight women is susceptible to breast cancer, at some point of time in her life. Early detection and effective treatment is the only rescue to reduce breast cancer mortality. Accurate classification of a breast cancer tumor is an important task in medical diagnosis. Machine learning techniques are gaining importance in medical diagnosis because of their classification capability. In this paper, we propose a new, Genetically Optimized Neural Network (GONN) algorithm, for solving classification problems. We evolve a neural network genetically to optimize its architecture (structure and weight) for classification. We introduce new crossover and mutation operators which differ from standard crossover and mutation operators to reduce the destructive nature of these operators. We use the GONN algorithm to classify breast cancer tumors as benign or malignant. To demonstrate our results, we had taken the WBCD database from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, ROC curves and AUC under ROC curves of GONN with classical model and classical back propagation model. Our algorithm gives classification accuracy of 98.24%, 99.63% and 100% for 50–50, 60–40, 70–30 training–testing partition respectively and 100% for 10 fold cross validation. The results show that our approach works well with the breast cancer database and can be a good alternative to the well-known machine learning methods.

A review of clustering techniques and developments

This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.

A novel genetic programming approach for epileptic seizure detection

The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal.

Performance evaluation of SVM based semi-supervised classification algorithm

To construct decision boundaries for two-class classification, SVM approach is attractive due to its efficiency. However, this approach is useful for 2-class classification and when the classes (labels) for the data are known. In practice, we have collection of labeled as well as unlabelled data, and it gives rise to semi-supervised classification problem. In this paper, we give a semi-supervised classification algorithm based on support vector machine (SVM). Novel feature of our approach is the formulation of spherical decision boundaries and the exploitation of the dynamical system associated with support function to obtain the number of clusters. The experimental results on a few well-known datasets, namely, Iris dataset, Shuttle landing control dataset, Wisconsin Breast cancer dataset, glass dataset, and balance scale dataset, indicate that our approach results in satisfactory classification as well as generalization accuracy.

A genetically optimized neural network model for multi-class classification

An enhanced Genetically Optimized Neural Network (GONN) is proposed for Multi-class data classification.Multi-tree GONN classifier is used to classify multi-class data.Enhanced GONN produces the highest classification accuracy among other classifiers in less time. Multi-class classification is one of the major challenges in real world application. Classification algorithms are generally binary in nature and must be extended for multi-class problems. Therefore, in this paper, we proposed an enhanced Genetically Optimized Neural Network (GONN) algorithm, for solving multi-class classification problems. We used a multi-tree GONN representation which integrates multiple GONN trees; each individual is a single GONN classifier. Thus enhanced classifier is an integrated version of individual GONN classifiers for all classes. The integrated version of classifiers is evolved genetically to optimize its architecture for multi-class classification. To demonstrate our results, we had taken seven datasets from UCI Machine Learning repository and compared the classification accuracy and training time of enhanced GONN with classical Kozas model and classical Back propagation model. Our algorithm gives better classification accuracy of almost 5% and 8% than Kozas model and Back propagation model respectively even for complex and real multi-class data in lesser amount of time. This enhanced GONN algorithm produces better results than popular classification algorithms like Genetic Algorithm, Support Vector Machine and Neural Network which makes it a good alternative to the well-known machine learning methods for solving multi-class classification problems. Even for datasets containing noise and complex features, the results produced by enhanced GONN is much better than other machine learning algorithms. The proposed enhanced GONN can be applied to expert and intelligent systems for effectively classifying large, complex and noisy real time multi-class data.

Enhanced cluster validity index for the evaluation of optimal number of clusters for Fuzzy C-Means algorithm

Cluster validity index is a measure to determine the optimal number of clusters denoted by (C) and an optimal fuzzy partition for clustering algorithms. In this paper, we proposed a new cluster validity index to determine an optimal number of hyper-ellipsoid or hyper-spherical shape clusters generated by Fuzzy C-Means (FCM) algorithm called as VIDSO index. The proposed validity index jointly exploits all the three measures named as intra-cluster compactness, an inter-cluster separation and overlap between the clusters. The proposed intra-cluster compactness is based on relative variability concept which is a statistical measure of relative dispersion or scattering of data in various dimensions within the clusters. The proposed inter-cluster separation measure indicates the isolation or distance between the fuzzy clusters. The proposed inter-cluster overlap measure determines the degree of overlap between the fuzzy clusters. The best fuzzy partition produced by the VIDSO index is expected to have low degree of intra-cluster compactness, higher degree of inter-cluster separation and low degree of inter-cluster overlap. The efficacy of VIDSO index is evaluated on six benchmark data sets and compared with a number of known validity indices. The experimental results and the comparative study demonstrate that, the proposed index is highly effective and reliable in estimating the optimal value of C and an optimal fuzzy partition for each data set because, it is insensitive with change in values of fuzzification parameter denoted by m. In contrast, the other indices [2], [3], [6], [7] fails to achieve the optimal value of C due to it is susceptibility with change in m.

Handling Big Data with Fuzzy Based Classification Approach

Big data is a collection of very large and complex data that is difficult to load into the computer memory. The major challenges include searching, categorization and analysis of big data. In this paper, a fuzzy based supervised classifier is proposed to handle the searching, storage and categorization of big data. In this classifier, we proposed a Random Sampling Iterative Optimization Fuzzy c-Means (RSIO-FCM) clustering algorithm which partitions the big data into various subsets. These subsets adequately cover all the instances (object space) of big data. Then, clustering is performed on these subsets by feeding forward the centers of clustered subset to group remaining subsets. Further, the designed classifier based on Bayesian theory is used to assign the labels to these clusters and also used to predict labels of unknown instances. Thus, the proposed approach results in effective clusters formation which also eliminates the problem of overlapping cluster centers faced by algorithm discussed in [1] named as Simple Random Sampling plus Extension FCM (rseFCM). The effectiveness of proposed clustering algorithm over rseFCM clustering is evaluated on two very large benchmark datasets in terms of fuzzification parameter m, objective function, computational time and accuracy. Experimental results demonstrate that, the RSIO-FCM algorithm generates more appropriate cluster centers location due to which it achieves better classification accuracy as compared to the rseFCM algorithm. Thus, it observed that, cluster centers location will have significant impact over classification results.

Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark

With the advancement in technology, a huge amount of data containing useful information, called Big Data, is generated on a daily basis. For processing such tremendous volume of data, there is a need of Big Data frameworks such as Hadoop MapReduce, Apache Spark etc. Among these, Apache Spark performs up to 100 times faster than conventional frameworks like Hadoop Mapreduce. For the effective analysis and interpretation of this data, scalable Machine Learning methods are required to overcome the space and time bottlenecks. Partitional clustering algorithms are widely adopted by researchers for clustering large datasets due to their low computational requirements. Thus, we focus on the design of partitional clustering algorithm and its implementation on Apache Spark. In this paper, we propose a partitional based clustering algorithm called Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) which is implemented on Apache Spark to handle the challenges associated with Big Data Clustering. Experimentation is performed on several big datasets to show the effectiveness of SRSIO-FCM in comparison with a proposed scalable version of the Literal Fuzzy c-Means (LFCM) called SLFCM implemented on Apache Spark. The comparative results are reported in terms of value of F-measure, ARI, Objective function, Run-time and Scalability. The reported results show the great potential of SRSIO-FCM for Big Data clustering.

Constructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining

In this paper, we propose a novel fast training algorithm called Constructive Semi-Supervised Classification Algorithm (CS-SCA) for neural network construction based on the concept of geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. Its a semi-supervised based approach, the training samples are semi-labeled i.e. for some samples, labels are known and for some samples, data labels are not known. The method starts with clustering, which is done by using the concept of geometrical expansion. In clustering process various clusters are formed. The clusters are visualizes in terms of hyperspheres. Once clustering process over labeling of hyperspheres is done, in which class is assigned to each hypersphere for classifying the multi-dimensional data. This constructive learning avoids blind selection of neural network structure. The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. Through our experimental work we conclude that CS-SCA result in simple neural network structure by less training time.

A multiclass classifier using Genetic Programming

This paper presents an approach for designing classifiers for a multiclass problem using Genetic Programming (GP). The proposed approach takes an integrated view of all classes when GP evolves. An individual of the population will be represented using multiple trees. The GP is trained with a set of N training samples in steps. A concept of unfitness of a tree is used in order to improve genetic evolution. Weak trees having poor performance are given more chance to participate in the genetic operations, and thus improve themselves. In this context, a new mutation operation called nondestructive directed point mutation is used, which reduces the destructive nature of mutation operation. The approach is being demonstrated by experimenting on some datasets.