Combined Classification Algorithm Research Articles

Steel grades classification and heating defects detection of bearing steel bars via pulse eddy current testing

Nondestructive examination of bearing steel raw material is a crucial step in ensuring the production of bearing rings. Traditional nondestructive examination methods struggle with real-time sorting, leading to potential production risks. This study proposes a pulse eddy current testing (PECT) approach combined multidimensional features and classification algorithm, to realize the recognition of steel grades and heating defects of bearing steel bars. Firstly, the two-probes differential PECT system was established. Subsequently, pulse signals were collected, and 27 features in the time domain, and frequency domain were extracted and analyzed. Finally, two classification algorithms, BP neural networking and Rindom Forest, are used to classify bearing steel. Experimental results demonstrate the approach proposed can sort bearing steel, die steel, and high-speed steel bars. Furthermore, it is capable of distinguishing between various types (brand, heat number) of bearing steel bars and identifying their overheated or burnt conditions.

Intrusion Detection System using the Hybrid Model of Classification Algorithm and Rule-Based Algorithm

Intrusion detection system ID is necessary to secure the system from various intrusions. Analysis of the communication to categorize the data as useful or malicious data is crucial. The cyber security employed using intrusion detection systems should not also cause the extra time to perform the categorization. Nowadays machine learning techniques are used to make the identification of malicious data or an intrusion with the help of classification algorithms. The data set used for experimenting is KDD cup 99. The effect of individual classification algorithms can be improvised with the help of hybrid classification models. This model combines classification algorithms with rule-based algorithms. The blend of classification using machine and human intelligence adds an extra layer of security. An algorithm is validated using precision, recall, F-Measure, and Mean age Precision. The accuracy of the algorithm is 92.35 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.

A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm.

In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.

The Method of Dynamic Identification of the Maximum Speed Limit of Expressway Based on Electronic Toll Collection Data

To overcome the drawbacks of the maximum speed limit information of expressways (i.e., long update cycle and great complexity of information recognition), in this work, an Electronic Toll Collection (ETC) gantry data-based method for dynamically identifying the maximum speed limit information of expressways is proposed. Firstly, the characteristics of the ETC gantry data are analyzed, and then data are cleaned and reconstructed, after which an algorithm is proposed for constructing a vehicle travel speed data set. Secondly, the speed feature vector model of the road section is established by taking the relationship among the speed distribution feature, time domain feature, and the maximum speed limit of the road section into consideration. Then, a data supplement algorithm is constructed to solve the problem of the imbalance of data samples. Finally, the combined GC-XGBoost classification algorithm is used to train and learn the potential speed limit features, and it is verified through the Fujian Provincial Expressway ETC data and the speed limit information provided by the Fujian Traffic Police. The result shows that the accuracy of the method in the recognition of the maximum limited speed information of the expressway is 97.5%. Compared with the traditional limited speed information recognition and extraction methods, the proposed approach can identify the maximum limited speed information of each section of the expressway more efficiently. It can also accurately identify the dynamic change of the maximum limited speed information, which is able to provide data support for intelligent expressway management systems and map providers.

Using SSVEP-BCI to Continuous Control a Quadcopter with 4-DOF Motions.

Brain-computer interfaces (BCIs) allow for translating electroencephalogram (EEG) into control commands, e.g., to control a quadcopter. This study, we developed a practical BCI based on steady-state visually evoked potential (SSVEP) for continuous control of a quadcopter from the first-person perspective. Users watched with the video stream from a camera on the quadcopter. An innovative user interface was developed by embedding 12 SSVEP flickers into the video stream, which corresponded to the flight commands of 'take-off,' 'land,' 'hover,' 'keep-going,' 'clockwise,' 'counter-clockwise' and rectilinear motions in six directions, respectively. The command was updated every 400ms by decoding the collected EEG data using a combined classification algorithm based on task-related component analysis (TRCA) and linear discriminant analysis (LDA). The quadcopter flew in the 3-D space according to the control vector that was determined by the latest four commands. Three novices participated in this study. They were asked to control the quadcopter by either the brain or hands to fly through a circle and land on the target zone. As a result, the time consumption ratio of brain-control to hand-control was as low as 1.34, which means the BCI performance was close to hands. The information transfer rate reached a peak of 401.79 bits/min in the simulated online experiment. These results demonstrate the proposed SSVEP-BCI system is efficient for controlling the quadcopter.

Short-Term Traffic Flow Prediction Considering Spatio-Temporal Correlation: A Hybrid Model Combing Type-2 Fuzzy C-Means and Artificial Neural Network

Traffic flow prediction is a key step to the efficient operation in the intelligent transportation systems. This paper proposes a hybrid method combing clustering methods and spatiotemporal correlation to predict future traffic trends based on artificial neural network. First, for the traffic flow collected from different loop detectors, a spatio-temporal correlation of data samples is evaluated by considering time correlation and spatial equivalent distance. Second, in order to improve classifying performance and reliability to anomalous data samples, a type-2 fuzzy c-means (FCM) is adopted to make fuzzification of the membership function. Then, a hybrid prediction model combined classification algorithm and neural network is designed to predict various patterns or trends in traffic flow data. Furthermore, the results from the prediction model are modified according to quantized spatio-temporal correlation. Finally, traffic volume data collected from the highway is used to optimize the parameter in the prediction model combination. Several traditional models are used as candidates in comparison, and the higher prediction accuracy demonstrates the effectiveness and feasibility of the hybrid prediction model.

Translation of disease associated gene signatures across tissues.

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.

A classifier fusion strategy to improve the early detection of neurodegenerative diseases

People in developed countries are living longer, and this has resulted in the prevalence of age-related diseases like Alzheimer's and dementia. Many believe that the early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This paper considers this idea and proposes a new classifier fusion strategy that combines classification algorithms and rules voting, product, mean, median, maximum and minimum to measure specific behaviours in people suffering with neurodegenerative diseases. More specifically, the fusion strategy analyses the stride-to-stride intervals in gait and its correlation with neurological functions. This approach is compared with base level classifiers a single classification algorithm using a set of feature vectors associated with gait patterns obtained from neurodegenerative patients and healthy people. The results show that the fusion strategy improves classification. Our experiments successfully show that a fusion strategy generates better results and classifies subjects more accurately than base level classifiers.

Facing the cold start problem in recommender systems

A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.

HuntMi: an efficient and taxon-specific approach in pre-miRNA identification

BackgroundMachine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. However, many current methods based on machine learning suffer from some drawbacks, including not addressing the class imbalance problem properly. It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. This study aims at improving performance of miRNA classification procedure, extending its usability and reducing computational time.ResultsWe present HuntMi, a stand-alone machine learning miRNA classification tool. We developed a novel method of dealing with the class imbalance problem called ROC-select, which is based on thresholding score function produced by traditional classifiers. We also introduced new features to the data representation. Several classification algorithms in combination with ROC-select were tested and random forest was selected for the best balance between sensitivity and specificity. Reliable assessment of classification performance is guaranteed by using large, strongly imbalanced, and taxon-specific datasets in 10-fold cross-validation procedure. As a result, HuntMi achieves a considerably better performance than any other miRNA classification tool and can be applied in miRNA search experiments in a wide range of species.ConclusionsOur results indicate that HuntMi represents an effective and flexible tool for identification of new microRNAs in animals, plants and viruses. ROC-select strategy proves to be superior to other methods of dealing with class imbalance problem and can possibly be used in other machine learning classification tasks. The HuntMi software as well as datasets used in the research are freely available at http://lemur.amu.edu.pl/share/HuntMi/.

Performance Evaluation of Algorithms for the Classification of Metabolic 1H NMR Fingerprints

Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.

A selective combined classification algorithm for mapping alterations on ASTER data

We present a method for combined classification aiming to map alterations in a set of ASTER (Advanced Spaceborne Thermal Emission and reflection) data in the Erongo Complex, Namibia. Ten alterations detected by the matched filtering unmixing method on the Hyperion dataset of the area are therefore used as training classes. The separability of the classes was computed to evaluate the ability of ASTER data to spectrally discriminate between these classes. The outcome of this computation is satisfactory for the high-probability training dataset. In order to improve the accuracy of upcoming processes, classes with high similarity (low separability) were combined. The classification of ASTER scene is then performed with the use of both individual and combined classification classifiers. A new combined classification method (named selective combined classification (SCC)) was developed in this research to achieve the highest possible accuracy in the resultant classification map. An accuracy analysis has proven the advantages and capability of SCC among all classifiers tested in this study (both individual and combined).

A combined reconstruction–classification method for diffuse optical tomography

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

A classification-based machine learning approach for the analysis of genome-wide expression data.

Three important areas of data analysis for global gene expression analysis are class discovery, class prediction, and finding dysregulated genes (biomarkers). The clinical application of microarray data will require marker genes whose expression patterns are sufficiently well understood to allow accurate predictions on disease subclass membership. Commonly used methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent framework. By integrating prediction accuracy, the MDSS algorithm learns the critical threshold of statistical significance (the alpha or P-value), eliminating the arbitrariness of setting a threshold of statistical significance and minimizing the effect of the normality assumptions. To reduce the false positive rate and to increase external validity of the predictive gene set, a jackknife step is used. This step identifies and removes genes in the initial MDSS with low combined predictive utility. The overall MDSS provides a prediction that is less dependent on an arbitrary study design (sample inclusion or exclusion) and should thus have high external validity. We demonstrate that this approach, unlike other published methods, identifies biomarkers capable of predicting the outcome of anthracycline-cytarabine chemotherapy in cases of acute myeloid leukemia. By incorporating two criteria-statistical significance and predictive utility-the approach learns the significance level relevant for a given data set. The MDSS approach can be used with any test and classifier operator pair.