Single Strong Classifier Research Articles

Research on Education Teaching Quality Analysis Based on the Neural Network Model

Universities emphasize quality review and monitoring. To effectively evaluate classroom teaching effectiveness, a trustworthy model of teaching quality evaluation is required. Due to the fact that teaching is a dynamic process, numerous elements influence teaching quality, and the link between assessment index and teaching effect is complex and nonlinear. There are numerous methods for measuring the quality of classroom instruction, but the vast majority of it relies on a single machine learning algorithm, making it difficult to construct an accurate and reliable mathematical model. In this paper, we employ the AdaBoost’s multicore neural network learning algorithm to learn several weak classifiers and combine them into a single strong classifier. We also transfer the classification probabilities into teaching quality outcomes to obtain the final teaching quality results. Our model offers a new, effective way for evaluating the quality of classroom instruction, and it can serve as a solid theoretical resource for reforming classroom instruction.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

Identification of Colorectal Cancer Using Near-Infrared Spectroscopy and Adaboost with Decision Stump

ABSTRACTRapid and objective detection of cancer is crucial for successful treatment. Near-infrared (NIR) spectroscopy is a vibrational technique capable of optically probing molecular changes associated with disease. The purpose of this study was to explore NIR spectroscopy for discriminating cancer from normal colorectal tissues. A total of 110 tissue samples from patients who underwent operations were characterized in this study. The popular ensemble technique AdaBoost was used to construct the diagnostic model. A decision stump was used as the weak learning algorithm. Adaboost with decision stump, an ensemble of weak classifiers, was compared with the most suitable single model, a strong classifier. Only the 20 most significant variables were selected as inputs for the model based on measured defined variable importance. Using an independent test set, the single strong classifier provided diagnostic accuracy of 89.1%, sensitivity of 100%, and specificity of 78.6%, whereas the ensemble of weak stumps provided accuracy of 96.3%, sensitivity of 96.3%, and specificity of 96.3% for distinguishing cancer from normal colorectal tissues. Therefore, NIR spectroscopy in combination with AdaBoost with decision stumps has demonstrated potential for rapid and objective diagnosis of colorectal cancer.

Real‐time vehicle detection with foreground‐based cascade classifier

The strategy based on Haar-like features and the cascade classifier for vehicle detection systems has captured growing attention for its effectiveness and robustness; however, such a vehicle detection strategy relies on exhaustive scanning of an entire image with different sizes sliding windows, which is tedious and inefficient, since a vehicle only occupies a small part of the whole scene. Therefore, the authors propose a real-time vehicle detection algorithm which is based on the improved Haar-like features and combines motion detection with a cascade of classifiers. They adopt a visual background extractor, accompanied by morphological processing, to obtain foregrounds. These foregrounds retain vehicle features and provide the positions within images where vehicles are most likely to be located. Subsequently, vehicle detection is performed only at these positions by using a cascade of classifiers instead of a single strong classifier, which is able to improve the detection performance. The authors' algorithm has been successfully evaluated on the public datasets, which demonstrates its robustness and real-time performance.

Design of coupled strong classifiers in AdaBoost framework and its application to pedestrian detection

In the AdaBoost framework, a strong classifier consists of weak classifiers connected sequentially. Usually the detection performance of the strong classifier can be improved increasing the number of weak classifiers used, but the improvement is asymptotic. To achieve further improvement we propose coupled strong classifiers (CSCs) which consist of multiple strong classifiers connected in parallel. Complementarity between the classifiers is considered for reducing intra- and inter-classifier correlations of exponential loss of weak classifiers in the training phase, and dynamic programming is used during the testing phase to compute efficiently the final object score for the coupled classifiers. In addition to CSC concept, we also propose using Aggregated Channel Comparison Features (ACCFs) that take the difference of feature values of Aggregated Channel Features (ACFs), enabling significant performance improvement. To show the effectiveness of our CSC concept, we apply our algorithm to pedestrian detection. Experiments are conducted using four well-known benchmark datasets based on ACFs, ACCFs, and Locally Decorrelated Channel Features (LDCFs). The experimental results show that our CSCs give better performance than the conventional single strong classifier for all cases of ACFs, ACCFs, and LDCFs. Especially our CSCs combined with ACCFs improve the detection performance significantly over ACF detector, and its performance is comparable to those of the state-of-the-art algorithms while using the simple ACF-based features.

Quantum adiabatic machine learning

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. We apply and illustrate this approach in detail to the problem of software verification and validation.

Online Boosting for Vehicle Detection

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

A boosting method for maximization of the area under the ROC curve

We discuss receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) for binary classification problems in clinical fields. We propose a statistical method for combining multiple feature variables, based on a boosting algorithm for maximization of the AUC. In this iterative procedure, various simple classifiers that consist of the feature variables are combined flexibly into a single strong classifier. We consider a regularization to prevent overfitting to data in the algorithm using a penalty term for nonsmoothness. This regularization method not only improves the classification performance but also helps us to get a clearer understanding about how each feature variable is related to the binary outcome variable. We demonstrate the usefulness of score plots constructed componentwise by the boosting method. We describe two simulation studies and a real data analysis in order to illustrate the utility of our method.

Ensemble methods for boosting the decoding performance of a hand grasping BMI

Event Abstract Back to Event Ensemble methods for boosting the decoding performance of a hand grasping BMI Erk Subasi1*, Benjamin Townsend2 and Hans Scherberger2 1 Institute of Neuroinformatics, Computer Science, Switzerland 2 Institute of Neuroinformatics, Switzerland We are developing a brain machine interface (BMI) to decode grasp shape in macaque monkeys online. Neural activity is evaluated using chronically implanted electrodes in the anterior intraparietal cortex (AIP) and ventral premotor cortex (F5), areas known to be involved in the transformation of visual signals into hand grasping instructions. Macaque monkeys were trained in a delayed grasping task, where they first placed their hands at rest and fixated a red LED before a grasping handle was presented in one of 5 different orientations, and the color of an LED instructed the animals to grasp the handle either with a power or precision grip, respectively. After a short delay the fixation LED dimmed instructing the monkey to perform the required grasp. Correct trials were rewarded with a small amount of juice. After successful training 5 floating micro-electrode arrays (FMA; MicroProbe Inc) were implanted in AIP (2) and F5 (3) of 1 animal. Each array comprised 16 platinum-iridium electrodes (length 1.0-4.5mm, spacing 0.5 mm). This configuration was chosen to facilitate the recording of neuronal activity within cortical sulci instead of on the cortical surface. Neural signals were sampled using a Cerebus (Cyberkinetics Inc, Foxborough, MA) Neural Signal Processor (NSP) and streamed to a dedicated decoding PC via UDP. We previously reported our results using a standard maximum a posteriori (MAP) classifier where we obtained 94% performance for grasp type decoding (precision vs. power), following a mean accuracy of 91% for grasp type and 2 grasp orientations (target tilted to left or right) and finally a mean accuracy of 55% where we decode full 10 conditions (grasp type and 5 orientations). MAP decoding is one of the standard methods in BMI literature which is shown yielding robust decoding results in similar experiments before. But we make 3 common strong assumptions regarding the underlying probability distributions in MAP framework. First, we assume a Poisson probability distribution for firing rates of each individual unit. Second, in order to ease the joint probability distribution computation, it is assumed that neurons are firing in an independent fashion. And third, it is assumed that these units have stationary firing probability distributions. It is important to note that a marginal increase in the decoding performance of a neural-prosthetic device may provide substantial improvement in perceived quality of the system by the user. Here, we introduce a new approach for decoding discrete grasping postures which does not need to rely on any of these assumptions. Ensemble methods have gained popularity among the machine learning community in the last decade, both due to their strong theoretical base and practical outcomes. They mainly try to optimally utilize many weak learners on a training set for achieving better performance compared to a single strong classifier while reducing the variance of the classifier and in some cases also providing a better bias. Here, the comparative decoding analysis will be presented with adaptive bagging and boosting techniques. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Poster Presentation Topic: Brain machine interface Citation: Subasi E, Townsend B and Scherberger H (2019). Ensemble methods for boosting the decoding performance of a hand grasping BMI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.056 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 May 2009; Published Online: 09 May 2019. * Correspondence: Erk Subasi, Institute of Neuroinformatics, Computer Science, Zurich, Switzerland, erk@ini.phys.ethz.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. 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