Majority Vote Algorithm Research Articles

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

Risk prediction of product‐harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China

AbstractWith the increasing of frequency and destructiveness of product‐harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product‐harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product‐harm event's evolution; ultimately, nine risk‐forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product‐harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self‐organising data mining (SODM). Further, an SODM‐based multiple classifier fusion (SB‐MCF) model was presented for the risk prediction related to a product‐harm event. The experimental results based on 165 Chinese listed companies indicated that the SB‐MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB‐MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case‐based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).

IRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Meiotic recombination is vital for maintaining the sequence diversity in human genome. Meiosis and recombination are considered the essential phases of cell division. In meiosis, the genome is divided into equal parts for sexual reproduction whereas in recombination, the diverse genomes are combined to form new combination of genetic variations. Recombination process does not occur randomly across the genomes, it targets specific areas called recombination "hotspots" and "coldspots". Owing to huge exploration of polygenetic sequences in data banks, it is impossible to recognize the sequences through conventional methods. Looking at the significance of recombination spots, it is indispensable to develop an accurate, fast, robust, and high-throughput automated computational model. In this model, the numerical descriptors are extracted using two sequence representation schemes namely: dinucleotide composition and trinucleotide composition. The performances of seven classification algorithms were investigated. Finally, the predicted outcomes of individual classifiers are fused to form ensemble classification, which is formed through majority voting and genetic algorithm (GA). The performance of GA-based ensemble model is quite promising compared to individual classifiers and majority voting-based ensemble model. iRSpot-GAEnsC has achieved 84.46% accuracy. The empirical results revealed that the performance of iRSpot-GAEnsC is not only higher than the examined algorithms but also better than existing methods in the literature developed so far. It is anticipated that the proposed model might be helpful for research community, academia and for drug discovery.

Reliable Multiple-choice Iterative Algorithm for Crowdsourcing Systems

The appearance of web-based crowdsourcing systems gives a promising solution to exploiting the wisdom of crowds efficiently in a short time with a relatively low budget. Despite their efficiency, crowdsourcing systems have an inherent problem in that responses from workers can be unreliable since workers are low-paid and have low responsibility. Although simple majority voting can be a solution, various research studies have sought to aggregate noisy responses to obtain greater reliability in results through effective techniques such as Expectation-Maximization (EM) based algorithms. While EM-based algorithms get the limelight in crowdsourcing systems due to their useful inference techniques, Karger et al. made a significant breakthrough by proposing a novel iterative algorithm based on the idea of low-rank matrix approximations and the message passing technique. They showed that the performance of their iterative algorithm is order-optimal, which outperforms majority voting and EM-based algorithms. However, their algorithm is not always applicable in practice since it can only be applied to binary-choice questions. Recently, they devised an inference algorithm for multi-class labeling, which splits each task into a bunch of binary-choice questions and exploits their existing algorithm. However, it has difficulty in combining into real crowdsourcing systems since it overexploits redundancy in that each split question should be queried in multiple times to obtain reliable results. In this paper, we design an iterative algorithm to infer true answers for multiple-choice questions, which can be directly applied to real crowdsourcing systems. Our algorithm can also be applicable to short-answer questions as well. We analyze the performance of our algorithm, and prove that the error bound decays exponentially. Through extensive experiments, we verify that our algorithm outperforms majority voting and EM-based algorithm in accuracy.

Multimodal Information Integration and Fusion for Histology Image Classification

Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.

Combination of optical and electronic logic gates for error correction in multipath differential demodulation

We present an optical multipath error correction technique for differentially encoded modulation formats such as differential-phase-shift-keying (DPSK) and differential polarization shift keying (DPolSK) for fiber-based and free-space communication. This multipath error correction method combines optical and electronic logic gates. The scheme can easily be implemented using commercially available interferometers and high speed logic gates and does not require any data overhead therefore does not affect the effective bandwidth of the transmitted data. It is not merely compatible but also complementary to error correction codes commonly used in optical transmission systems such as forward-error-correction (FEC). The technique consists of separating the demodulation at the receiver in multiple paths. Each path consists of a Mach-Zehnder interferometer with a different integer bit delay used in each path. Some basic logic operations follow and the three paths are compared using a simple majority vote algorithm. Experimental results show that the scheme improves receiver sensitivity by 1.5 dB at BER of 10(-3),in back-to-back configuration. Numerical results indicate a 1.6 dB improvement in the presence of Chromatic Dispersion for a 25% increase in tolerance for a 3dB penalty from +/-1220 ps/nm to +/-1520 ps/nm. and a 0.35 dB improvement for back-to-back operation.

The development of multiple redundant flight control systems for high integrity applications

Multiple redundant flight control systems have been the subject of continuous development since the 1950s. Early examples include the automatic landing systems of the BAC 1–11 and the VC10; these were followed by the full flight envelope control systems fitted to the Concorde and Tornado. These examples all use analogue computing techniques, with redundancy levels appropriate to the individual fail-passive or fail-operational requirements of each system.However, recent advances in digital technology have enabled highly sophisticated flight control systems to be implemented which would be wholly impractical using analogue computing methods. Furthermore, digital computing is amenable to self-monitoring techniques which can be used not only to improve system maintainability, but also to augment the cross comparison or majority voting algorithms which have been developed on analogue systems.