class based performance data
Student Performance Prediction Model Based on Discriminative Feature Selection
... education data, this paper takes the education data related to the mathematics scores of some students in the second grade of two high schools in China as the research object, and proposes an integrated ...
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Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF)
... multi-label data, where class-based features and local label correlations are ...problem. Class-based features are considered to benefit the discrimination of different class ...
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Class imbalance ensemble learning based on the margin theory
... each class in a data-set plays an important role in machine ...world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of ...
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Survey paper on query optimization techniques in the cloud
... with performance of the class of Nearest Neighbor (NN) queries, the central class of queries utilized as a part of a large number of the area based administrations, covering the query keeping ...
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A kernel based two class classifier for imbalanced data sets
... training data samples ( 1158) rather than all the training data samples were used to form the candidate center set ...training data set size is large. The proposed maximal LOO-AUC-based ...
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Using Output Codes for Two-class Classification Problems
... two-class data using clustering techniques, does improve accuracy for some base learners, when compare to applying them directly to two-class datasets, but not for all of ...whose performance ...
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Family Values: Ownership Structure, Performance and Capital Structure of Canadian Firms
... This table reports results of random-effects regressions that estimate the impact of ownership on Tobin’s q, financial leverage and ROA. The sample is family-owned firms from 1998 to 2005. Tobin’s q is (total assets + ...
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A Review of Class Imbalance Problem
... ensemble based learning approach (DataBoost-IM) that combined boosting with data ...improved performance and speed. An approach based on repeated sub-sampling proposed by Khalilia et ...the ...
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Data based predictive hybrid driven control for a class of imperfect networked systems
... has been clearly proven through both simulations and real-time experiments. However, for a NCS with large delays and/or high packet dropout ratio, the RVSPC performance may be degraded when the driving commands ...
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A new genetic algorithm based clustering for binary and imbalanced class data sets
... the performance of the standard K-means with different ...new data are clustered by comparing their smallest distance from the means of the existing ...whole data set, but this new proposed method ...
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Using Semi-supervised Classifiers for Credit Scoring
... are based on modelling both the distribution of past loan repayers and past ...the performance of the two- class classifiers will ...training data set such that when drift is introduced to the ...
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World Class Data Center Features Our best practices for world class performance, reliability and service. echomountain - Rock Solid
... Our data centers are protected with a dual-alarmed, dual-interlock multi- zoned, dry-pipe, water-based fire suppression system armed with sensory mechanisms (HSSD) to sample the air and give alarms prior to ...
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Class attendance and cardiology examination performance: a study in problem-based medical curriculum
... There are two limitations in this study. First, due to the attendance policy at the school, students who were present less than 75% during an academic session were not allowed to take part in the exam. Therefore, ...
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Attendance of students in the class and their performance
... feature describing of image content and image data model. Feature describing image content step produces vectors for multi-dimensional picture. These vectors decides the content whether it is a low-level or a ...
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Hybrid Preprocessing Method for Support Vector Machine for Classification of Imbalanced Cerebral Infarction Datasets
... majority class samples that are deleted are added to the minority class sample [11], to enhance performance relative to the performance of the techniques when used ...to data cleansing ...
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Ensemble based novel class identification for Class Imbalance under sampled Data
... A prediction method e.g. decision tree or neural network is applied to each bootstrap sample to get base model. A bagged ensemble predicts a new sample by having each of its base models classify that example. The final ...
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A NOVEL FRAMEWORK FOR CLASSIFICATION OF BENIGN AND MALIGNANT DATA UNITS BASED ON LEARNING METHODS
... To understand the concept of naive bayes classification, consider the above figure where we can see that data is plotted in the two different shades i.e. green and red. From the above data we can see that ...
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On a class of bi polarization variance based measurement: Evidence from Cameroonian data
... In this paper, bipolarization for the median is measured by the square of the normalized gap between the income means of the two groups, the group of individuals with their income less than the median and the opposite ...
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Dual Class Firms and Debt Issuance
... dual class share structures on the parameters of debt ...dual class firms to use debt more frequently but the agency problem may impact every aspect of a loan contract, including interest spread, maturity, ...
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The impact of class imbalance in classification performance metrics based on the binary confusion matrix
... only performance metrics based on the confusion ma- trix are considered, a marginal approach to Receiver Operating Characteristics (ROC) analysis [16] can also be carried ...
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