Subcellular Phenotype Images Classification by MLP Ensembles with Random Linear Oracle

Predicting stock returns by classifier ensembles

Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is needed for large‐scale genome analysis. The automated cell phenotype image classification problem is an interesting “bioimage informatics” application. It can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, three well‐known texture feature extraction methods including local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM) have been applied to cell phenotype images and the multiple layer perceptron (MLP) method has been used to classify cell phenotype image. After classification of the extracted features, decision‐templates ensemble algorithm (DT) is used to combine base classifiers built on the different feature sets. Different texture feature sets can provide sufficient diversity among base classifiers, which is known as a necessary condition for improvement in ensemble performance. For the HeLa cells, the human classification error rate on this task is of 17% as reported in previous publications. We obtain with our method an error rate of 4.8%.

To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.

Purpose. At the present stage, network attack detection systems based on the following neural networks are most often offered: multilayer perceptron, Kohonen network or self-organizing map and their combinations. The efficiency problem of two approaches to detect attacks on a computer network using neural network technology based on the normalized data of the open NSL-KDD database is considered. Methodology. As an architectural solution to the network attack detection system, it is proposed to consider the following approaches: based on one neural network determining the attack class (first approach) and an ensemble of five neural networks (second approach), which at the first stage determines the attack category (DoS, Probe, U2R , R2L), and in the second stage, the attack class belonging to a certain category. Findings. Based on the neural networks created in the MatLAB program, a study was conducted of their error on the length of the training sample using various training algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradient with different numbers of hidden neurons (minimum, average and maximum). Certain optimal parameters of neural networks with two approaches were determined. Originality. In the course of conducting experiments with various approaches, the results obtained were: TP (True Positive); FP (False Positive); FN (False Negative); TN (True Negative), based on which the following indicators were calculated for assessing the quality of solutions: correct determination of network attacks; false positives; reliability; accuracy and completeness, which prove the feasibility of using an ensemble of neural networks (second approach). Practical value. On the created neural networks with various approaches, studies were conducted: the operating time of neural networks; errors of the first kind; errors of the second kind. According to the results of the first study, the average operating time of an ensemble of neural networks is 0.92 s, and the operating time of a neural network (according to the first approach) is 2.21 s. According to the results of the second study, the error of the first kind using an ensemble of neural networks is 2.17%, and using the neural network (the first approach) – 7.39%. According to the results of the third study, the error of the second kind using an ensemble of neural networks is 3.91%, and using the neural network (the first approach) – 6.96%, which is confirmed by the efficiency of using an ensemble of neural networks (second approach).

Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?

An important problem in quantitative medical image analysis is a large number of features (often highly correlated) to instance ratio. To handle this, we developed a feature selector and an ensemble classifier based on a modified version of random subspace method. We propose using a fusion of feature selection concepts: ranking based, correlation based and random subspaces, to develop a concordance correlation coefficient based random subspace method (CCC RSM) feature selector. It forms random feature subsets with weakly correlated yet relevant features while the ensemble classification is achieved by training the base classifier with these feature subsets. Axillary lymph node (ALNs) metastases is one of the most important prognostic factors in breast cancer. We applied CCC RSM for four binary class classifications based on the number of metastatic ALNs: (i) = 1 vs 0 (ii) 1-3 vs 0 (iii) 1-3 vs = 4, and (iv) 4 vs 0. We extracted textural kinetics from habitats of fifty eight dynamic contrast enhanced magnetic resonance imaging breast tumors. We used three classifiers to compare the accuracies achieved by CCC RSM with random subspaces (RS), wrappers and correlation based feature selector (CFS). For each binary classification we achieved the best accuracy (= 78%) using CCC RSM.

Damage in structures can negatively affect their functionality and safety and leads to failure. Thus it is very important to monitor structures for occurrence, location and severity of damage. Structural health monitoring techniques provide information on the life expectancy of structures simultaneously detects and locates structural damage. Damage identification of structures based on vibration has always been important subjects and are being rapidly used to damage and location of structures. Artificial Neural Networks (ANNs) as a soft computing method using dynamic parameters of structures have been utilized increasingly for structural damage detection due to their excellent pattern recognition capability. Dynamic parameters of structure are easy to implement for damage identification and can directly linked to the topology of structure. This study presents the application of ANN for damage identification in steel beams using dynamic parameters. For identification of severity and location of damage, at first, five individual neural networks corresponding to mode 1 to mode 5 are considered. At the second step, a method based on neural network ensemble is proposed to combine the outcomes of the individual neural networks to a single network. Ensemble results were evaluated and discussed according to the differences between predicted output by ANN and desire data (target data) obtained from experimental modal analysis of structure.KeywordsArtificial neural networksMean square errorModal analysisDamage detectionNeural network ensemble

Diversity is a key characteristic of a classifier ensemble. A classifier ensemble must be composed of base classifiers with different performance in different areas of the problem space. Several works studied different diversity measures by performing extensive numerical experiments. However, up to our knowledge, no method has been proposed to visualize the diversity of a classifier ensemble. In this contribution, we propose a novel approach to visualize and analyze classifier ensembles diversity. Our novel proposal (called DivGrams), allows the visualization of the diversity between base classifiers in the classifier ensemble by using a network representation. The nodes represent base classifiers and its edges represent diversity relations between the base classifiers. We apply DivGrams to fuzzy rule-based classifier ensembles, as in our previous work these machine learning models proved to deal well with complex classification problems. A preliminary experimental study is performed using two UCI datasets in order to show the usefulness of DivGrams. Furthermore, we show a practical application of DivGrams to develop manual classifier selection on eight UCI datasets. The results obtained show the interesting characteristics of our proposal and support this novel approach in the fuzzy classifier ensemble field.

Context. Image segmentation plays a key role in computer vision. The quality of segmentation is affected by many factors: noise, artifacts, complex shapes of objects. Classical methods cannot always guarantee good success, depending on the quality of the image and the existing noise, they cannot always achieve the desired result. The proposed method uses an ensemble of neural networks, which makes it possible to increase the accuracy and stability of segmentation. Objective. The goal of the work is to develop a new method of combining predictions of neural network ensembles, which can improve segmentation accuracy by combining images of different image sizes. Method. A method is proposed that averages the shapes of objects depicted on prediction masks. A pyramid of images is used to improve segmentation quality, each level of the pyramid corresponds to an increased size of the original image. This approach allows obtaining image characteristics at different levels. For a test image, a prediction is obtained from each neural network in the ensemble, after which a pyramid is built for the image. All pyramid levels are combined into the final image using SAAMC. All obtained final images for each neural network are also combined at the end using SAAMC. The use of an ensemble of neural networks combined with the pyramid method allows for reducing the impact of noise and artifacts on the segmentation results. Results. The use of this method was compared with the usual use of individual neural networks and the ensemble averaging method. The obtained results show that the proposed method outperforms its competitors. Application of the proposed method improved the accuracy and quality of segmentation. Conclusions. The conducted research confirmed the sense of using an ensemble of neural networks and creating a new method of combining predictions. The use of an ensemble of neural networks makes it possible to compensate for the errors and shortcomings of individual neural networks. Using the proposed method can significantly reduce the impact of noise and artifacts on segmentation. Further study and modification of this method will make it possible to further improve the quality of segmentation.

Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection‐based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size‐related various properties of dermoscopy images. To this end, EfficientNet models B3–B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross‐validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.

Rough set theory has proved efficient for many applications, including finding hidden patterns in data, data reduction, evaluating significance of data, and generating sets of decision rules from data. Recently, it has shown to be effective approach for constructing ensemble learning systems as well. Learning classifier systems are genetics-based machine learning techniques that have recently shown a high degree of competence on a variety of data mining problems. Attempts to improve its generalization capabilities in the literature using ensemble learning lack a systematic and robust techniques for partitioning the problem at hand. It is well known that ensemble performance depends on the problem decomposition technique being used. Rough set based learning classifier system ensemble is proposed in this paper. In this approach, rough set attribute reduction is used to generate a set of reducts, and then a diverse subset of these reducts is selected to train an ensemble of base classifiers. The experiments show that classification accuracy of reduct-based ensemble systems outperforms a single learning classifier system model. It has also shown better performance than either an ensemble of classifiers with all attributes being used or a single classifier trained by a single reduct. It has also shown competitive performance to the random subspace ensemble strategy on the set of real data sets used in the experiments.

An advanced multi-modal method for human authentication featuring biometrics data and tokenised random numbers

The paper describes the approach to automatic face recognition for access control application area using wavelet transform and neural network's ensemble. Wavelet transform implements the compression of the face images and thus accelerates the classifier's work, while neural network's ensemble with proposed decision rule provides low recognition error. Proposed ensemble's decision rule carries out a high level of unknown people's access rejection, which is the most significant requirement for the access control systems, and gives a good balance between known and unknown people's recognition errors.

Artificial neural networks apply complex non-linear functions to pattern recognition problems. An ensemble is a 'committee' of neural networks that usually outperforms single neural networks. Bronchiolitis is a common manifestation of viral lower respiratory tract infection in infants and toddlers. To train artificial neural network ensembles to predict the disposition and length of stay in children presenting to the Emergency Department with bronchiolitis. A specifically constructed database of 119 episodes of bronchiolitis was used to train, validate, and test a neural network ensemble. We used EasyNN 7.0 on a 200 Mhz pentium PC with a maths co-processor. The ensemble of neural networks constructed was subjected to fivefold validation. Comparison with actual and predicted dispositions was measured using the kappa statistic for disposition and the Kaplan-Meier estimations and log rank test for predictions of length of stay. The neural network ensembles correctly predicted disposition in 81% (range 75-90%) of test cases. When compared with actual disposition the neural network performed similarly to a logistic regression model and significantly better than various 'dumb machine' strategies with which we compared it. The prediction of length of stay was poorer, 65% (range 60-80%), but the difference between observed and predicted lengths of stay were not significantly different. Artificial neural network ensembles can predict disposition for infants and toddlers with bronchiolitis; however, the prediction of length of hospital stay is not as good.

To predict potential problems and avoid an unexpected breakdown of rotating machinery, a rule-based classifier ensemble approach is presented. Feature reduction was first implemented on a fault decision table using discernibility matrices and the genetic algorithm. The generated rules of the reducts were used to build the candidate base classifiers. Then, several base classifiers were selected according to their diversity and scale. The weights of the selected base classifiers were also calculated based on the support rate measurements. A classifier ensemble was constructed through an integration of the base classifiers using an improved weighted voting technique. Finally, the proposed classifier ensemble was verified based on the vibration data of bearing types SKF6203 and NU205. The accuracy for the SKF6203 bearing type reached 88.75 %, which is at least 5 % higher than that of the three base classifiers for this type of bearing. In addition, the recognition rate for the latter bearing type was 90 %. The reasoning process was much easier to comprehend owing to the semantic descriptions of the rules. The results show that this is a promising and transparent approach for diagnosing typical faults of rotating machinery.