A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

A new approach for mitigating blackout risk by blocking minimum critical distance relays

Electric power systems are complex and commonly run near their operational limits. Power systems are basically designed based on the (N − 1) criterion. In these systems, total or partial blackouts are unavoidable. Cascading failures following an initial event is recognized as the main mechanism for power system blackouts. Undesirable activation of zone 3 of distance relays due to their local function has been identified as one of the major causes for propagation of cascading failures. In this paper, in order to mitigate the risk of power system blackouts, a novel approach is proposed for recognizing the critical line outages with the highest contribution in cascading failure and preventing their undesirable outage by blocking zone 3 of their distance relay. In this paper, based on the statistical activation of distance relays, a deterministic–probabilistic approach is developed for identifying the critical lines with the highest contribution in blackouts due to activation of zone 3 of their distance relays. In order to avoid interference of relay blocking with the main protection duty of distance relays, the proposed approach recognizes the minimum critical lines with the highest contribution in cascading failure for blocking zone 3 of their distance relays. The proposed approach is demonstrated on the New England 39-bus system and detailed simulation studies carried out to examine the validity and effectiveness of the proposed approach.     

Tree ensemble construction using a GRASP-based heuristic and annealed randomness

Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness.The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named “GAR”, GRASP with annealed randomness.The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method.The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

An approach to design interface topologies across interdependent urban infrastructure systems

This paper proposes an approach to design or retrofit interface topologies to minimize cascading failures across urban infrastructure systems. Four types of interface design strategies are formulated based on maximum network component degree, maximum component betweenness, minimum Euclidean distance across components and component reliability rankings. To compute and compare strategy effectiveness under multiple hazard types, this paper introduces a global annual cascading failure effect (GACFE) metric as well as a GACFE-based cost improvement (GACI) metric. The GACI metric quantifies the improvement of the strategy effectiveness per kilometer increment of interdependent link length (ILL) relative to a reference strategy with minimum ILL. Taking as examples the power and gas transmission systems in Harris County, Texas, USA, optimum interface designs under random and hurricane hazards are discussed. Findings include that the strategy based on reliability rankings minimizes the GACFE metric, and decreases the GACI value relative to a reference practical strategy by 10-15% under different power grid safety margins. Such metrics will contribute to coupled utility system design or retrofit given that current guidelines or recommended practices in the utility industry mostly rely on minimum Euclidean distances and are yet to include interdependent effects in their provisions.     

Performance and reliability of electrical power grids under cascading failures

The stability and reliability of electrical power grids are indispensable to the continuous operation of modern cities and critical for preparedness, response, recovery and mitigation in emergence management. Because present power grids in China are often running near their critical operation points, they are especially vulnerable and sensitive to external disturbances such as hurricanes, earthquakes and terrorist attacks, which may trigger cascading failures or blackouts. This paper describes a quantitative investigation of the stability and reliability of power grids with a focus on cascading failures under external disturbances. The 118-bus (substation) power network in Hainan, China is employed as a case study to investigate the risk of cascading failure of the regional power grids. System performance and reliability of the power grids are evaluated under two hypothetical scenarios (seismic impact and intentional disturbance) that could trigger cascading failures. By identifying the most vulnerable (critical) edges and nodes, the robustness of the power network is evaluated under the triggered cascading failures. It is found that the system reliabilities could decline as much as 95% during the triggered cascading failure. This paper also explores the use of concepts from modern complex network theories such as state transition graph and characteristic length to understand the complex mechanism of cascading failures. The findings could be useful for power industries and emergency managers to evaluate the vulnerability of power systems, understand the risk of blackout induced by cascading failures, and improve the resilience of power systems to external disturbances.     

斜窄上升流过程与单元集成式分离设备

拉美拉斜板以及早年的Lavodune/Ladoflux分离设备,应用的都是斜窄上升流过程,但前者是单元集成结构,而后者是单个设备。介绍了在拉美拉的基础上,对斜窄流过程及其单元集成设备结构特征的系统研究以及斜窄流浓缩、分级、沉渣-除油等分离设备及其在矿业多种领域的实际应用。     

Deriving reservoir operating policies using classifier systems

An application of classifier systems to time variant, water resources allocation problems is described, the aim of the work being to assess the suitability of the technique for deriving control strategies. The difficulties of allocating credit to classifiers where co-operating sequences of rules have to be developed are discussed and methods for overcoming some of the difficulties are covered. The training of the classifier on two problems, the first containing a single, surface water reservoir and the second two reservoirs is used to develop techniques and the results show that classifiers possess an ability to learn about the domain. However the resulting operating strategies are not appropriate for the operation of water resources systems. The work indicates that in their current format, classifier systems cannot learn to operate systems where long, interdependent chains of decisions are involved.     

Supervised data analysis and reliability estimation with exemplary application for spectral data

The analysis and classification of data is a common task in multiple fields of experimental research such as bioinformatics, medicine, satellite remote sensing or chemometrics leading to new challenges for an appropriate analysis. For this purpose different machine learning methods have been proposed. These methods usually do not provide information about the reliability of the classification. This, however, is a common requirement in, e.g. medicine and biology. In this line the present contribution offers an approach to enhance classifiers with reliability estimates in the context of prototype vector quantization. This extension can also be used to optimize precision or recall of the classifier system and to determine items which are not classifiable. This can lead to significantly improved classification results. The method is exemplarily presented on satellite remote spectral data but is applicable to a wider range of data sets.