Received date: August 2011; Revised date: October 2011; Accepted date: November 2011
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Clinical databases have accumulated large quantities of information about patients and their clinical histories. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support and improving the quality of medical diagnosis. This paper presents individual, ensembles and hybrid of computational intelligence techniques such as Support Vector Machine (SVM), Neural Networks (NN), Function Network (FN) and Fuzzy Logic (FL) to classify real bioinformatics datasets. The performance of the proposed computational techniques measured using well known bioinformatics datasets. As expected, the performance of the proposed ensembles and hybrid computational intelligence models is better compared to the monolithic models and overcome the weaknesses of existing classifiers particularly in the classification accuracy.
Ensemble Network, Hybrid Models, SVM, Fuzzy Logic System, Bioinformatics, Classification.
In recent years, rapid developments in bioinformatics have generated a large amount of data. Often, drawing conclusions from these data require sophisticated computational analyses. Bioinformatics, or computational biology, is the interdisciplinary science of interpreting biological data using information technology and computer science. The importance of this new field of inquiry will grow as we continue to generate and integrate large quantities of data. A particularly active area of research in bioinformatics is the application and development of machine learning techniques to biological problems. Analyzing large biological datasets requires making sense of the data by inferring structure or generalizations from the data. Examples of these types of dataset analysis include cancer classification, diabetes classification, uncertainty manipulation, etc [15,20]. Each of these problems can be framed as a problem in machine learning [1,2,3]. Therefore there is a great potential to increase the interaction between machine learning and bioinformatics. The types of learning algorithms fall along several classifications but we can summarize them as supervised and unsupervised learning . Supervised learning used in medical diagnoses adopts the general paradigm of pattern recognition where objects are described by a collection of features that form a multidimensional space in which all discrimination activities take place. Various classifiers, both linear and nonlinear can be used, including SVM, Fisher’s linear discriminates, polynomial classifiers, NN, fuzzy rule‐based systems etc. [6,7,8,9,33]. The classifier is developed through training on a dataset of examples that are representative of the objects to be classified. During the training process, the classifier learns the feature patterns that distinguish between the different object classes. For instance, given a set of attributes about potential cancer patients, and whether those patients actually had cancer, the classifier could learn how to distinguish between likely cancer patients and possible false alarms. For example, breast cancer dataset from the Wisconsin Hospital University contains 699 samples with 683 complete data and 16 samples with missing attributes. The attributes contained by the dataset are Lump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli and Mitoses. There are two values in the class variable of breast cancer which are benign (non-cancerous) and malignant (cancerous). This sort of learning could take place with FL systems, NN or SVM. Recently, SVM and NN have emerged as a powerful tool in pattern recognition , classification and forecasting in many areas. They have featured in a wide range of medical and business journals, often with promising results [11,12,13,19]. Inspired by promising results obtained in other fields [26,27], we explored the use of these intelligence techniques for classifying disease types from datasets related to bioinformatics. The main focus of this paper resides in the classification of bioinformatics datasets using individual, ensemble and hybrid of computational intelligence techniques such as SVM, NN, FN, and FL.
The rest of this paper is organized as following. A review of related earlier work is presented in Section 2. Section 3 introduces the ensemble models used in this work. Section 4 introduces the hybrid model used in this work. Section 5 describes the experimental set up. Section 6 presents models development steps of the proposed models. Section 7 describes the datasets used in the experiments. Section 8 discusses the experimental results of using the developed computational intelligence techniques. Finally, Section 9 concludes the paper and highlights the future work.
Recently, many classifiers were developed exploring various fields with the help of computer science. In fact, most of the research work found in the literature related to disease classification either makes use of statistical models or artificial neural networks. Statistical methods such as linear discriminate analysis, generalized linear regression such as logistic regression, and nearest neighbor classification are widely used. There are many methods and algorithms used to mine biomedical datasets for hidden information. They include NNs, Decision Trees (DT), FL Systems, Naive Bayes, SVM, cauterization, logistic regression and so on. Studying the literature it turns out that the most frequent choices for the medical decision support systems are the DT (C4.5 algorithm), NNs and the Naive Bayes [1,2,8,9,10,17,22,42,43,44]. These algorithms are very useful in medicine because they can decrease the time spent for processing symptoms and producing diagnoses, making them more precise at the same time. Also, many of the research assessed the algorithms on a narrow set of medical databases . However and to the best or our knowledge ensembles of these techniques have not been used in the bioinformatics dataset classification.
NNs are networks of units, called neurons that exchange information in the form of numerical values with each other via synaptic interconnections, inspired by the biological neural networks of the human brain. They become very powerful and flexible approaches to function approximation. NNs are mainly refer to the feed forward networks such as multilayer perceptrons and radial basis function neural networks, which have been widely used to develop diagnostic models. In order to improve the costs benefit ratio of breast cancer screenings, authors of  evaluated the performance of a back-propagation NN to predict an outcome (cancer/not cancer) to be used as classifier. NNs were trained on data from family history of cancer, and socio demographic, gyneco obstetric and dietary variables. Research is going on in capitalizing the use of NNs in medical diagnosis of breast cancer. This work indicates that statistical NNs can be effectively used for breast cancer diagnosis to help oncologists  in which classification is based on a feed forward NN rule extraction algorithm. General regression NN, or probabilistic NN was used in order to get the suitable result. The problem with NNs is that they usually adopt gradient-based learning methods which are susceptible to local minima and long training times especially when the number of classes/categories is high. The authors of  introduce artificial NNs with back propagation for classification of heart disease cases. This solution is implemented in a medical system to support the classification of the Doppler signals in cardiology. The predictions yielded by the method were more accurate than similar presented in . The NNs’ major disadvantage is complexity, which makes classification process difficult to interpret. Nevertheless, the authors prove that they produce effective classifications in case of medical data. As far as NN is concerned, the influence of the noisy inputs on the output variable together with the transfer functions, implicit in the values of the weights. Hence an unattractive feature of such networks is that the number of weights and complexity increase greatly as the network grows. Also the weights may not always be easy to interpret if the data is imprecise and uncertain which leads to the problem of under fitting or over fitting and the problem becomes difficult to visualize from an examination of the weights.
SVM has been proposed as a very effective method for pattern recognition, machine learning and data mining [2,8]. The general idea is to map non-linearly D-dimensional input space into a high dimensional feature space. A linear classifier (separating hyper plane) is constructed in this high dimensional space to classify the data. The use of the kernel trick allows constructing the classifier without explicitly knowing the feature space. It is considered to be a good candidate because of its high generalization performance. Intuitively given a set of points which belong to either one of the two classes, a SVM can find a hyper plane having the largest possible fraction of points of the same class on the same plane. This hyper plane called the optimal separating hyper plane (OSH) can minimize the risk of misclassifying examples of the test set. SVM, when using One-Versus-All (OVA) approach to make binary classifiers applicable to multi category problems, it can be seen that, when the number of classes increases, the complexity of the overall classifier also increases. So the system becomes more complex and requires extra computations. In SVM classifiers, problems with corrupted inputs are more difficult than problems with no input uncertainty. Even if there is a large margin separator for the original uncorrupted inputs, the observed noisy data may become non-separable. For example by using a kernel function in SVM, the input vector is mapped to in a usually high dimensional feature space and the uncertainty in the input data introduces uncertainties in the feature space. To overcome this problem, researchers used total least square regression methods with SVM but could not achieve promising results.
FNs are extensions of NN which consist of different layers of neurons connected by links. Each computing unit or neuron performs a simple calculation: a scalar typically monotone function f of a weighted sum of inputs. The function f, associated with the neurons, is fixed and the weights are learned from data using some well-known algorithms such as the least-square fitting. A FN consists of a layer of containing the input data; a layer of output units containing the output data; one or several layers of neurons or computing units which evaluate a set of input values, coming from the input units, and which give a set of output values to the output units. The computing units are connected to each other, in the sense that the output from one unit can serve as part of the input to another neuron. Once the input values are given, the output is determined by the neuron type, which can be defined by a function [26,27,28,29].
Type-2 FLS was introduced as an extension of the concept of Type-1 FLS . Type-2 FLS has membership grades that are themselves fuzzy. For each value of a primary variable (e.g., pressure and temperature), the membership is a function (not just a point value). The secondary Membership Function (MF) has its domain in the interval (0, 1), and its range may also be in (0, 1). Hence, the MF of a Type-2 FLS is three dimensional, and it is the newly introduced third dimension that provides new degrees of design freedom for handling uncertainties. Type-2 FLS does not obtain good performance when the number of training data is small, but it can perform better when the number of training prototypes is large [30,31,32].
Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction/classification accuracy. An ensemble model is constructed using a set of machine learning techniques to train for a portion of a given problem and then to integrate these machine learning techniques to solve of the entire problem. This method is also called committee of learning machines. For classification problem it is also called multiple classifier systems, classier fusion, etc. One approach of ensemble system is built by a group of base learners to solve a problem which is similar to divide-and-conquer method. Instead of solving the same problem, the base learners are trained for different subproblems. This approach is classified as mixture of experts. However, in ensemble, the base learners are used to solve the same problem. Ensemble methods are basically used to improve the generalization capability of a single machine learning technique. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complex data structures [5,23,24]. In this paper we have used three NN for the ensemble model. Training data of those models are obtained through bagging sampling principle. The next task is to combine these member models using an appropriate strategy which include linear and nonlinear ensemble. In this paper we have adopted two methods for combining the three NN. For the first model we have combined them using weighted average (Figure 1) and for the second model we have combined them using another NN (Figure 2) to produce the final output.
Multiple Kernels Learning (MKL) has been an attractive topic in machine learning . Multiple kernel learning searches for a combination of base kernel functions/matrices that maximizes a generalized performance measure. Typical measures studied for multiple kernel learning, include maximum margin classification errors [35,36]. It has been regarded as a promising technique for identifying the combination of multiple data sources or feature subsets and been applied in a number of domains, such as genome fusion, splice site detection, image annotation and so on [40,41]
To achieve our aim, a hybrid models was built: FN-FL-SVM (FFS). FN was used as the base for each of the models. This is due to its functional approximation capability and its ability to select the best variables for the system directly from the training data. Next we describe the framework design of the hybrid model and the optimized parameters of each of the techniques before using in the hybrid. The model is composed of three major blocks containing respectively: Functional Networks (FN), Type-2 Fuzzy Logic and Support Vector Machines (SVM). The FN block, using its leastsquares fitting algorithm, is used to select the best variables from the input data. The dimensionality of the input data can be ignored by the user as it is automatically handled by the FN block that plays the role of a best-variable selector in the model. The best variables are extracted from the input data and then divided into training and test sets using the Stratified Sampling approach. The training set is passed to the Fuzzy Logic block where uncertainties are removed, if any exists. Already, Type-2 Fuzzy Logic System has been shown in several works such as in [14,26,27,31] to have the ability to remove uncertainties using its extension to a third dimension. It would be futile to attempt to revalidate this already established fact in the literature. Hence, the focus of this work is the successful combination of the individual techniques to implement hybrid models that demonstrate the combined capabilities of each technique. The training data, with uncertainties removed, is then used to train the SVM block in readiness for prediction with the test data. Finally, the test data is passed to the trained SVM block to perform the regression task in order to evaluate the performance of the model. The role performed by the Fuzzy Logic block in this model is to ensure that in case an input data containing uncertainties is used, such uncertainties would have been removed before the data is passed to the SVM block for training. In this way, only “clean” data is allowed to enter the SVM block which performs the prediction task after the training process. This is an attempt to complement the performance of the hybrid with the ability of Type-2 Fuzzy Logic to handle uncertainties. However, in the absence of uncertainties in the input data, then the performance of the Type-2 FLS block is reduced to that of Type-1 FLS [26,27,37,38,39]. Figure 3 shows the design framework of this model.
Various computational models for classification have been developed for the classification of real bioinformatics datasets; these include monolithic, hybrid and ensemble models. We conducted our experiments on Matlab R2007a. The datasets are stored in MS Excel documents and read directly from Matlab. All the graphs are generated by using the same Matlab R2007a. We used Naïve Bayes classifier and the SVM attribute evaluation feature selection technique available in the machine learning library with Java implementation . We adopted the same splitting used by earlier published work using the dataset to allow direct comparison of results, i.e. same number of cases for training dataset and same number of cases for evaluation models. We conducted our experiments on Matlab R2007a. The datasets are stored in MS Excel documents and read directly from Matlab. All the graphs are generated by using the same Matlab R2007a. We used Naïve Bayes classifier and the SVM attribute evaluation feature selection technique available in the machine learning library with Java implementation . We adopted the same splitting used by earlier published work using the dataset to allow direct comparison of results, i.e. same number of cases for training dataset and same number of cases for evaluation dataset. The diagnostic performance of the developed models is evaluated using Receiver Operating Characteristic (ROC) curve . In ROC curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points. Each point on the ROC plot represents a sensitivity/specificity pair corresponding to a particular decision threshold. A model with perfect discrimination (no overlap in the two distributions) has a ROC plot that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC plot is to the upper left corner, the higher the overall accuracy of the model. Figures 5~10 show the ROC of NN and SVM models with the datasets.
Combining the output of several classifiers is useful only if they disagree on some inputs. Theoretical and empirical work showed that an effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well. Diverse individuals can be obtained by adopting different model structure. In case of NN, different types of models can be obtained by having different network types, number of neuron in hidden layer, learning algorithm and initial state in weight space. Diversity can be supported by training the ensemble/hybrid members on different training datasets which can be achieved by bagging, boosting or cross validation. We divide the datasets randomly into training and testing set. We have used 80% of the datasets for training and 20% for testing. Homogeneous ensemble models with the same kind of computational techniques with different fixed parameters are chosen in each run. Performing optimization by other computational techniques with different fixed parameters results into a completely different architecture of the models in each run. Furthermore, the computational techniques of the models are trained by different portion of the training datasets and thus the ensemble and hybrid models are enforced to be diverse enough in order to substantiate better generalization. The algorithm can be continued to N runs so as to have an ensemble of N members. Figure 4 shows the models development steps.
Datasets from the machine learning repository , at the University of California Irvine have been used. A brief description of these datasets is shown in Table 1.
For these datasets, the size represents the number of instances/entries in a dataset, the number of attributes represents how many values is contained in one instance, missing shows whether there is any incomplete entry, and class represents the number of categories to be classified in a dataset. For example, the Wisconsin Breast Cancer (WBC) dataset contains 699 samples with 683 complete data and 16 samples with missing attributes. Te attributes contained by the dataset are Lump Thickness, Uniformity of Cell Size Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli and Mitoses. There are two values in the class variable of WBC which are benign (non malignant (cancerous). The patient’s problem is then diagnosed as either benign or malignant. For the datasets having more than two classes, such as hypothyroid datasets, the problem is then diagnosed as normal, hyper-thyroid or hypo-thyroid.
It is evident from Figures (5 ~ 16) that the performance of the ensemble models is better compared to the monolithic models because the ROC plot passes through the upper left corner (100% sensitivity, 100% specificity) more. It is also shown in Tables 2, 3 and Figures (17 ~ 26) that the performance of the hybrid model is better compared to the monolithic models in terms of the training and testing classification accuracy. As we have used MATLAB toolboxes of NN, and both of Type-1 and Type-2 FLS, it is noticed that the classification accuracy of different datasets with different sizes is satisfactory. The reason is that, for small and simple datasets, Type-1 has better generalization property as it keeps the overall process simple. However if we look at the ROC graphs, we can say that hybrid model with Type-2 FLS outclassed the other models. Therefore our intuition for handling the uncertainty in a classification framework by using Type-2 FLS is justified.
This paper demonstrates the use of individual computational intelligence techniques, ensemble and hybrid of computational models for classifying of real bioinformatics datasets. Individual, ensemble and hybrid computational models were developed and their performance checked under different modeling conditions. The results obtained indicate that the diagnostic performance of the hybrid and ensemble models is much better compared to the performance of the individual models in terms of the classification accuracy. As a future work, more research will be done with the aim of developing more heterogeneous ensemble and hybrid models
The authors would like to acknowledge Taif University and King Fahd University of Petroleum and Minerals for providing the financial and the computing support that have been introduced to achieve this work. Special thanks go to anonymous reviewers for their insightful comments and feedback, resulting in a significant improvement in the quality of this paper.
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