Ensemble Of Different Classifiers Research Articles

An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC’s early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection.We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods.

Deep ensemble learning approach for lower extremity activities recognition using wearable sensors

AbstractHuman walking is a very challenging task and always requires rigorous practice. It is a learning process that involves the complex coordination of the brain and lower limbs. The bipedal robots that mimic the human morphological structure to produce human similar walking, are not capable of producing an efficient walk. Due to walking challenges and structural differences, a robot cannot walk like a human being. In this research, to achieve the aforementioned objective to produce a human similar walk, human lower extremity activities are considered to understand walking behaviour. The experiment involves different walking styles on different terrains. To capture the learning process of bipedal robot locomotion, a deep learning‐based ensemble classifier is introduced for human lower activities recognition. To understand the learning process seven different walking activities are considered for analysis purposes. An Inertial measurement unit (IMU) is used as a wearable device due to its small form factor and unobtrusive nature to capture the walking movement of different lower limbs joints. Three public datasets viz. mHealth, OU‐ISIR similar action and HAPT inertial sensor data sets are considered for this study. To classify the activities, 2 different deep learning models namely convolutional neural network (CNN) and long short‐term memory (LSTM) are used. To generalize the results, an ensemble of different classifiers is implemented. The Classifier has reported accuracy of 99.25%, 88.48% and 97.44%, respectively, on the aforementioned data sets. This work can be utilized for elderly subjects' postural stability, rehabilitation of patients post‐stroke and trauma, generation of robot walk trajectories in cluttered environment and reconstruction of impaired walking.

Dynamically enhanced static handwriting representation for Parkinson’s disease detection

Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson’s disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of “dynamically enhanced” static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.

A Novel Prediction Method for Zinc-Binding Sites in Proteins by an Ensemble of SVM and Sample-Weighted Probabilistic Neural Network

In the prediction of zinc-binding sites in proteins, there are few real binding-site residues, whereas most residues are non-binding-site residues, resulting in a typical imbalanced classification problem. This paper proposes a novel method, SSWPNN (an ensemble of support vector machine and sample-weighted probabilistic neural network), based on downsampling and an ensemble of different classifiers, in view of the imbalance of zinc-binding sites in proteins. Multiple random downsampling techniques without replacement are performed on the whole set, and the support vector machine is trained as the base classifier on each subset to calculate the weights of samples, while the sample-weighted probabilistic neural network is constructed as a strong classifier for prediction. The experimental results showed that our method is superior to other methods not only in the overall prediction performance for the four types of residues but also in the prediction performance for any type of residue. The results of experimental testing on an independent test set collected by the authors in recent years showed that our method achieved better prediction performance than others not only for the four types of residues overall but also for any one type of residue. In addition, the importance of the features selected by the method is analyzed by reducing certain feature to calculate the scores of the performance index. The source code and datasets are available at http://net.jitsec.cn:88/UploadedImages/SSWPNN.rar .

A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.

Enhancing Ensemble Performance through Feature Selection and Hybridization

Ensemble has been proved a successful approach for enhancing the performance of a single classifier. But there are two key factors directly influencing the outcomes of an ensemble: accuracy of each single member and diversity between the members. There have been many approaches used in the literature to create the mentioned diversity. In this paper, we add to them a novel approach, in which classifier type variance is utilized along with feature subset diversification to create a high diversity ensemble of different classifiers and the ensemble is optimized using a multi-objective evolutionary algorithm. The suggested approach outperformed existing ones in experiments conducted on some standard datasets.

Cooperating experts for soundtrack analysis of MPEG movies

Abstract In this paper, we propose a multi-expert classification system (MES) for the audio classification of MPEG movies. The system has been designed according to an hybrid architecture which is made of three cascaded stages and constitutes an ensemble of different classifiers, each one implemented by means of a multi-expert architecture. Classification of the audio tracks exploits four pure classes (music, speech, silence and noise) plus three hybrid classes associated to complex patterns resulting from the overlap of different components (e.g., speech overlapped with music or noise). The soundtracks of 30 movies selected from various genres have been used for building a wide database of samples and for the successive assessment of system performance. A significant amount of experimental results obtained by the proposed MES, by other classification systems using a single classifier, and by another MES using a parallel fusion scheme, are reported in the paper together with comments and comparative analyses. In addition, the paper demonstrates the application of the knowledge arising from an analysis of intermediate classification results in order to obtain indications about the definition of the MES architecture. The results achieved by using our system are extremely encouraging when compared with those obtained by the other MES.