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International Convention on Information and Communication Technology, Electronics and Microelectronics

作者： K. Andric D. Kalpic Privredna Banka Zagreb Radni?ka 50 10000 Zagreb Hrvatska Sveu?ili?te u Zagrebu Fakultet elektrotehnike i ra?unarstva Unska 3 10000 Zagreb Hrvatska

ISBN: (纸本)9781509025435

This paper makes a practical contribution to classification in credit risk assessment by providing empirical evidence on which class distribution in the training sample should be used to maximize performance of different classification algorithms. Data used for credit risk assessment are often imbalanced due to the number of defaulting clients, who represent the minority class, being much less numerous than the non-defaulting majority class. Classification algorithms are usually biased towards the majority class and can show deceivingly high overall prediction accuracy, while at the same time exhibiting poor performance in prediction of the minority class. Although altering class distribution can be an effective method for alleviating the adverse impact of class imbalance, limited research efforts have been devoted to empirical evaluation of the role of class distribution in credit risk assessment, especially when dealing with real life data samples. To address this issue, an empirical study on the effect of different proportion of training examples belonging to each class on classifiers' performance is presented. In addition to logistic regression, neural networks and gradient boosting methods were evaluated using several real life and publicly available datasets from the UCI Machine Learning Repository.

关键词： Classification algorithms Risk management Logistics Boosting Neurons Training Biological neural networks sorting algorithm Risk Management Biological neural networks Logistics Boost Risk Assessment Functional training Neurons Power grids

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Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

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JOURNAL OF APPLIED MATHEMATICS 2013年第unknown期2013卷 590614-1-590614-18页

作者： Fong, Simon Zhuang, Yan Tang, Rui Yang, Xin-She Deb, Suash Univ Macau Dept Comp & Informat Sci Macau Peoples R China Middlesex Univ Fac Sci & Technol London N17 8HR England Cambridge Inst Technol Dept Comp Sci & Engn Ranchi Bihar India

Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

关键词： Selecting Optimal Feature Set High-Dimensional Data Swarm Search sorting algorithm Searches Nondeterministic polynomial-time hard dimensional data PLUGGING dike swarms Feature extraction

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Comparison of Capacitor Voltages Balancing Methods for Cascaded H-bridge STATCOM

Comparison of Capacitor Voltages Balancing Methods for Casca...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Abdulveleev, I. R. Khramshin, T. R. Kornilov, G. P. Nosov Magnitogorsk State Tech Univ Magnitogorsk Russia

ISBN: (纸本)9781509056484

The object of the research work is the multilevel high-voltage converters with Cascaded H-Bridges (CHB) controlled by two different methods. They are the sorting algorithm and the fundamental harmonic displacement method. The comparison was carried out on the basis of mathematical modeling for the case of cascade connection of 10 cells with the rated voltage on the capacitors equal to 1000 V. Such circuits can be implemented, in particular, in high power STATCOMs with the voltage of 10 kV. Such converters are widely applied to compensate reactive power of electrical facilities at ironworks, for example at arc furnaces or continuous hot or cold rolling mills. In the process of control system design, it is necessary to solve a dual problem, particularly, to provide balancing of capacitor voltages for the minimum time and within a definite range. It is also necessary to avoid the overloading of the slave switching devices caused by the increase in the number of commutations. The modeling results showed that the suggested methods of balancing can provide the acceptable voltage quality at the point of connection, THD of output voltage is 5.23% and 7% for the first and the second method respectively. When the sorting algorithm is used, balancing takes less time as compared with the displacement method. However, this is achieved at the cost of higher load imbalance of semiconductor switches, which is undesirable in terms of thermal conditions. When balancing was carried out using the displacement method, the load of semiconductor switches was more uniform. This method can be recommended for practical application as the main one.

关键词： multilevel converters cascaded H-bridges STATCOM sorting algorithm fundamental harmonic displacement method voltage balancing

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Multiclass cancer-microarray classification algorithm with pair-against-all redundancy

Multiclass cancer-microarray classification algorithm with p...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Mattia Bosio Pau Bellot Philippe Salembier Albert Oliveras-Vergés Department of Signal Theory and Communications Technical University of Catalonia Barcelona Spain

ISBN: (纸本)9781467352345

Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the rest, called Pair Against All, PAA. The OAA+PAA approach has been tested on seven publicly available datasets. It has been compared with the common OAA approach and with state of the art alternatives. The obtained results showed how the OAA+PAA algorithm consistently improves the OAA results, unlike other ECOC algorithms presented in the literature.

关键词： Microarray multiclass classification ECOC multi-class Pairing Numteral Subscriber Line Multiplexer Microarray state of the art binary sorter sorting algorithm

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Machine Learning Classification algorithms for Phishing Detection: A Comparative Appraisal and Analysis

Machine Learning Classification Algorithms for Phishing Dete...

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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf)

作者： Noah Ndakotsu Gana Shafi’I Muhammad Abdulhamid Department of Cyber Security Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9781728107141

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing attack Classification algorithm Accuracy Performance metric RandomForest spear phishing sorting algorithm phishing computer information security False-positives Classifiers Performance metrics application developer quadratic errors

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Profiling Novel Classification algorithms: Artificial Immune System

Profiling Novel Classification Algorithms: Artificial Immune...

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IEEE International Conference on Cybernetic Intelligent Systems

作者： Peter van der Putten Lingjun Meng Joost N. KoK LIACS Eeiden University Shandong University LIACS Leiden University

ISBN: (纸本)9781424429141

In this paper we present an approach for bench-marking and profiling novel classification algorithms. We apply it to AIRS, an Artificial Immune System algorithm inspired by how the natural immune system recognizes and remembers intruders. We provide basic benchmarking results for AIRS, to our knowledge the first such test under standardised conditions. We also investigate how data set properties (data set size) relate to AIRS performance, and what other algorithms produce similar patterns over- and underperformance on specific data sets. We present three methods for computing algorithm similarity that may be useful for profiling novel algorithms in general.

关键词： artificial immune systems sorting algorithm BENCHMARKS Immune System Dataset air

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EAC: Efficient Associative Classifier for Classification

EAC: Efficient Associative Classifier for Classification

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International Conference on Applied Machine Learning (ICAML)

作者： Santosh Kumar Usha Manasi Mohapatra Debabrata Singh Dilip Kumar Choubey Deptartment of CSIT ITER S' x2018 O'A University Bhubaneswar Odisha India School of CSE Vellore Institute of Technology VeIlore India

ISBN: (纸本)9781728139098

Mining of large datasets which generally leads to the generation of a huge volume of rules and redundancy which Associative Classifiers find very difficult to handle. Thus, most Associative Classification algorithms like CPAR and CMAR, only mine frequent itemsets, which are then processed using additional algorithms in a greedy manner. This adds overhead in running time and makes the process more complex. In this paper, we proposed a new Associative Classification algorithm, called Efficient Associative Classifier (EAC). EAC deals with redundant association rules through an effective and simple pruning technique which also helps in cutting down the number of rules which finally form a part of the classifier. The classifiers are built in a two-phased manner so as to achieve the maximum accuracy and maximum representation of all possible class labels involved in the domain. In the first phase, association rule mining (ARM) is performed globally by taking global values of support and confidence. The rule set is pruned on the basis of information gain. Similarly, second phase deals with the ARM processed locally for each class label followed by pruning. The optimal pruned rule set is sorted based in certain parameters and supplied to the classifier. The proposed method used the information gain and entropy to build a set of optimal rules. In spite of being an efficient associative classifier, EAC achieves very good accuracy and tops the accuracy comparison charts for most of the UCI-Machine learning datasets that we used for testing.

关键词： data mining learning (artificial intelligence) pattern classification Pattern recognition Classifiers Associative Ruleset data mining information gain East African Community sorting algorithm cutting down Propionibacterium acnes Taxonomy mining

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LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHETYPAL DICTIONARIES

LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHET...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Ribana Roscher Christoph Romer Bjorn Waske Lutz Plumer Division of Remote Sensing and Geoinformatics Institute of Geographical Sciences Freie Universitat Berlin Department of Geoinformation Institute of Geodesy and Geoinformation University of Bonn

ISBN: (纸本)9781479979301

This paper introduces archetypal dictionaries for a self-taught learning framework for the application of landcover classification. Self-taught learning, an unsupervised representation learning method, is exploited to learn low-dimensional and discriminative higher-level features, which are used as input into a classification algorithm. Experiments are conducted using a multi-spectral Landsat 5 TM image of a study area in the north of Novo Progresso located in South America. Our results confirm that self-taught learning with archetypal dictionaries provide features, which can be used as input into a linear logistic regression classifier. The obtained classification accuracies are comparable to kernel-based classifier using the original features.

关键词： Self-taught learning Archetypal analysis Landcover classification Dictionaries as Topic Classifiers Learning Logistic Regression South America sorting algorithm Learning methods MULTISPECTRAL

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Comparison of the Application Effect of Existing Classification algorithms by Measured Voltage Dip

Comparison of the Application Effect of Existing Classificat...

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China International Electrical and Energy Conference (CIEEC)

作者： Chengsheng Li Huaying Zhang Yan Li Electric Power Research Institute China Southern Power Grid Company Shenzhen Power Supply Corporation Shenzhen China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

This paper presents the mechanism of five different algorithms for the type of unbalance dip, compared the results based on measured voltage dip by these algorithms. Apply three algorithms to calculate the type of unbalance dip for some typical cases. A conclusion is that there are large differences in results when applying the different algorithms to measured voltage dips. For two of the algorithms it is recommended against application or further development.

关键词： Electromagnetic compatibility (EMC) power quality dip type measured voltage dip voltage sags Power quality algorithms sorting algorithm Unbalance Snuff Dip

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FEATURE CLASSIFICATION BASED ON LOCAL INFORMATION

FEATURE CLASSIFICATION BASED ON LOCAL INFORMATION

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European Signal Processing Conference

作者： Ling Shao Ihor Kirenko Ping Li University of Pisa

Feature extraction is the first and most critical step in various vision applications. The detected features must be classified into different feature types before they can be efficiently and effectively applied on further vision tasks. In this paper, we propose a feature classification algorithm that classifies the detected regions into four types including blobs, edges and lines, textures, and texture boundaries, by using the correlations with the neighbouring regions. The effectiveness of the feature classification is evaluated on image retrieval.

关键词： sorting algorithm Taxonomy information image retrieval Feature extraction Sight textures

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