Related Classification Algorithms Research Articles

Classification of black tea leaf water content based on hyperspectral imaging

Withering is an important process step in black tea production, and the degree of withering directly determines the quality and flavor of black tea. Presently, the judgment of the degree of the withering of black tea at home and abroad mainly depends on the experience of tea masters, which is highly subjective and quickly leads to the inconsistent quality of finished tea. Based on these reasons, this thesis takes Ying Hong No. 9 black tea as the research object, extracts the spectral information of 50 tea leaves in four time periods using hyperspectral imaging technology and an image processing algorithm, and classifies them using a support vector machine, K-value proximity, random forest algorithm, and BP neural network, and the experimental results show that the spectral information of tea leaves in different periods has some variability. The BP neural network in The classification accuracy on the test set reached 73.3%, which was significantly better than the other three algorithms. Therefore, it is feasible to use hyperspectral imaging technology and related classification algorithms to nondestructively identify and classify the water content of black tea leaves during the tail-withering process.

High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI.

Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.

Semi-Supervised Classification of Dual-Frequency PolSAR Image Using Joint Feature Learning and Cross Label-Information Network

Dual-frequency polarimetric synthetic aperture radar (PolSAR) data can provide more information than single-frequency data, which can effectively improve classification accuracy. However, how to obtain sufficient and non-redundant feature representation from dual-frequency PolSAR data remains to be resolved. Besides, deep learning has shown good performance in PolSAR image classification, but it often requires a large number of labeled samples to participate in the training process, which is time-consuming and labor-intensive. In this paper, we propose a novel dual-frequency PolSAR image semi-supervised classification method that combines a dual-frequency joint feature learning (DFJFL) module with a cross label-information network (CLIN). First, the DFJFL module is developed based on the consistency and complementarity of dual-frequency data. It eliminates information redundancy by feature constraint loss function, and obtains compact dual-frequency joint feature representation. Subsequently, in order to avoid the influence of speckle noise, the proposed CLIN not only applies consistency regularization under network perturbation, but also uses the scattering mechanism of PolSAR data to find similar sample pairs to complete the consistency regularization under input perturbation, thereby achieving semi-supervised classification for PolSAR data. Experiments on four real dual-frequency PolSAR datasets verify that the proposed method can effectively extract dual-frequency PolSAR information, and make full use of unlabeled samples to improve classification accuracy. At the same time, compared with several related image classification algorithms, the proposed method could achieve the best performance.

Aeolus L2 Activities at KNMI

KNMI has had a long involvement with the recently launched Aeolus mission. Aeolus is an ESA Earth-Explorer mission and embarks the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN is the first space-borne Doppler Wind Lidar (DWL) and was successfully launched in late August 2018. KNMI has had a long association with this mission and is sharing the software development of the L2B processor used to generate L2B wind products at ECMWF, develops related scene classification algorithms and performs calibration/validation activities. In addition, KNMI conducts more fundamental work for improving the retrieval of atmosphere optical properties, taking into account multiple scattering effects.

Grey relational classification algorithm for software fault proneness with SOM clustering

The estimation by the human judgment to deal with the inherent uncertainty of software gives a vague and imprecise solution. To cope with this challenge, we propose a new hybrid analogy model based on the integration of grey relational analysis (GRA) classification with self-organising map (SOM) clustering. In this paper, a new classification approach is proposed to distribute the data to similar groups. The attributes are selected based on GRC values. In the proposed, the similarity measure between reference project and cluster head is computed to determine the cluster to which target project belongs. The fault-proneness of reference project is estimated based on the regression equation of the selected cluster. The proposed algorithm gives resilience to users to select features for both continuous and categorical attributes. In this study, two scenarios based on the integration of proposed classification with regression have been proposed. Experimental results show significant results indicating that proposed methodology can be used for the prediction of faults and produce conceivable results when compared with the results of multilayer-perceptron, logistic regression, bagging, naive Bayes and sequential minimal optimisation (SMO).

Grey relational classification algorithm for software fault proneness with SOM clustering

The estimation by the human judgment to deal with the inherent uncertainty of software gives a vague and imprecise solution. To cope with this challenge, we propose a new hybrid analogy model based on the integration of grey relational analysis (GRA) classification with self-organising map (SOM) clustering. In this paper, a new classification approach is proposed to distribute the data to similar groups. The attributes are selected based on GRC values. In the proposed, the similarity measure between reference project and cluster head is computed to determine the cluster to which target project belongs. The fault-proneness of reference project is estimated based on the regression equation of the selected cluster. The proposed algorithm gives resilience to users to select features for both continuous and categorical attributes. In this study, two scenarios based on the integration of proposed classification with regression have been proposed. Experimental results show significant results indicating that proposed methodology can be used for the prediction of faults and produce conceivable results when compared with the results of multilayer-perceptron, logistic regression, bagging, naïve Bayes and sequential minimal optimisation (SMO).

An Adaptive Hybrid Ant Colony Optimization Algorithm for The Classification Problem

Classification is an important data analysis and data mining technique. Taking into account the comprehensibility of the classifier generated, an adaptive hybrid ant colony optimization algorithm called A_HACO is proposed which can effectively solve classification problem and get the comprehensible classification rules at the same time. The algorithm incorporates the artificial bee colony optimization strategy into the ant colony algorithm. The ant colony global optimization process is used to adaptively select the appropriate rule evaluation function for the data set given. Based on the classification rules obtained, the artificial bee colony optimization strategy is used to tackle the continuous attributes for further optimization of classification rules. This approach is evaluated experimentally using different standard real datasets, and compared with some proposed related classification algorithms. It shows that A_HACO can adaptively select the appropriate rule evaluation function and has better accuracy compared with related works.

Implementation of Machine Learning Algorithms on CICIDS-2017 Dataset for Intrusion Detection using WEKA

For protecting and securing the network, with Intrusion Detection Systems through hidden intrusion has become a popular and important issue in the network security domain. Detection of attacks is the first step to secure any system. In this paper, the main focus is on seven different attacks, including Brute Force attack, Heartbleed/Denial-of-service (DoS), Web Attack, Infiltration, Botnet, Port Scan and Distributed Denial of Service (DDoS). We rely on features derived from CICIDS-2017 Dataset for these attacks. By using various subset based feature selection techniques performance of attack has been identified for many features. Using these techniques, it has been determined the appropriate group of attributes for finding every attack with related classification algorithms. Simulations of these techniques present that unwanted feature can be removed from attack detection techniques and find the most valuable set of attributes for a definite classification algorithm with discretization and without discretization, which improve the performance of IDS.

Novel learning framework (knockoff technique) to evaluate metric ranking algorithms to describe human response to injury

Objective: The objective of this study is to present a novel framework, termed the knockoff technique, to evaluate different metric ranking algorithms to better describe human response to injury.Methods: Many biomechanical metrics are routinely obtained from impact tests using postmortem human surrogates (PMHS) to develop injury risk curves (IRCs). The IRCs form the basis to evaluate human safety in crashworthiness environments. The biomechanical metrics should be chosen based on some measure of their predictive ability. Commonly used algorithms for the choice of ranking the metrics include (a) areas under the receiver operating characteristic curve (AUROC), time-varying AUROC, and other adaptations, and (b) some variants of predictive squared error loss. This article develops a rigorous framework to evaluate the metric selection/ranking algorithms. Actual experimental data are used due to the shortcoming of using simulated data. The knockoff data are meshed into existing experimental data using advanced statistical algorithms. Error rate measures such as false discovery rates (FDRs) and bias are calculated using the knockoff technique. Experimental data are used from previously published whole-body PMHS side impact sled tests. The experiments were conducted at different velocities, padding and rigid load wall conditions, and offsets and with different supplemental restraint systems. The PMHS specimens were subjected to a single lateral impact loading resulting in injury and noninjury outcomes.Results: A total of 25 metrics were used from 42 tests. The AUROC-type algorithms tended to have higher FDRs compared to the squared error loss–type functions (45.3% for the best AUROC-type algorithms versus 31.4% for the best Brier score algorithm). Standard errors for the Brier score algorithm also tended to be lower, indicative of more stable metric choices and robust rankings. The wide variations observed in the performance of the algorithms demonstrated the need for data set–specific evaluation tools such as the knockoff technique developed in this study.Conclusions: In the present data set, the AUROCs and related binary classification algorithms led to inflated FDRs, rendering metric selection/ranking questionable. This is particularly true for data sets with a high proportion of censoring. Squared error loss–type algorithms (such as the Brier score algorithm or its modifications) improved the performance in the metric selection process. The presented new knockoff technique may wholly change how IRCs are developed from impact experiments or simulations. At the very least, the knockoff technique demonstrates the need for evaluations among different metric ranking/selection algorithms, especially when they produce substantially different biomechanical metric choices. Without recommending the AUROC-type or Brier score–type algorithms universally, the authors suggest careful assessments of these algorithms using the proposed framework, so that a robust algorithm may be chosen, with respect to the nature of the experimental data set. Though results are given for sets from a published series of experiments, the knockoff technique is being used by the authors in tests that are applicable to the automotive, aviation, military, and other environments.

An improved weighted extreme learning machine for imbalanced data classification

This paper proposes an improved weighted extreme learning machine (IW-ELM) for imbalanced data classification. By incorporating voting method into weighted extreme learning machine (weighted ELM), three major steps are involved in the proposed method: training weighted ELM classifiers, eliminating unusable classifies to determine proper classifiers for voting, and finally determining the classification result based on majority voting. Simulations on many real world imbalanced datasets with various imbalance ratios have demonstrated that the proposed algorithm outperforms weighted ELM and other related classification algorithms.

A Weighted Relational Classification Algorithm Based on Rough Set

A Weighted Relational Classification Algorithm Based on Rough Set

A relational approach to probabilistic classification in a transductive setting

Transduction is an inference mechanism adopted from several classification algorithms capable of exploiting both labeled and unlabeled data and making the prediction for the given set of unlabeled data only. Several transductive learning methods have been proposed in the literature to learn transductive classifiers from examples represented as rows of a classical double-entry table (or relational table). In this work we consider the case of examples represented as a set of multiple tables of a relational database and we propose a new relational classification algorithm, named TRANSC, that works in a transductive setting and employs a probabilistic approach to classification. Knowledge on the data model, i.e., foreign keys, is used to guide the search process. The transductive learning strategy iterates on a k-NN based re-classification of labeled and unlabeled examples, in order to identify borderline examples, and uses the relational probabilistic classifier Mr-SBC to bootstrap the transductive algorithm. Experimental results confirm that TRANSC outperforms its inductive counterpart (Mr-SBC).