scholarly journals Quiet eye studies in sport within the motor accuracy and motor error paradigms

2021 ◽  
Vol 15 (5) ◽  
pp. 372-390
Author(s):  
Joan N. Vickers
Keyword(s):  
Imbalanced Data ◽  
Behavioral Measure ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Radial Error ◽  
Quiet Eye ◽  
Motor Error ◽  
Motor Accuracy ◽  
The Relationship

This paper reveals new insights that comes from comparing quiet eye (QE) studies within the motor accuracy and motor error paradigms. Motor accuracy is defined by the rules of the sport (e.g,. hits versus misses), while motor error is defined by a behavioral measure, such as how far a ball or other object lands from the target (e.g. radial error). The QE motor accuracy paradigm treats accuracy as an independent variable and determines the QE duration during an equal (or near-equal) number of hits and misses per condition per participant, while the motor error QE paradigm combines hits and misses into one data set and determines the correlation between the QE and motor error, which is used as a proxy for accuracy. QE studies within the motor accuracy paradigm consistently find a longer QE duration is a characteristic of skill, and/or interaction of skill by accuracy. In contrast, QE motor error studies do not analyze or report the relationship between the QE duration and accuracy (although often claimed), and rarely find a significant correlation between the QE duration and error. Evidence is provided showing the absence of significant results in QE motor error studies is due to the low number of accurate trials found in motor error studies due to the inherent complexity of all sport skills. Novices in targeting skills make fewer than 20% of their shots and experts less than 40% (with some exceptions) creating imbalanced data sets that make it difficult, if not impossible, to find significant QE results (or any other neural, perceptual or cognitive variable) related to motor accuracy in sport.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data

2017 ◽  
Vol 21 (5) ◽  
pp. 868-875
Author(s):  
Hiroyasu Matsushima ◽  
Keiki Takadama ◽  
◽  
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Sigmoid Function ◽  
Learning Classifier System ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Dynamic Matching ◽  
Learning Classifier ◽  
Stable Performance ◽  
The Given

In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECS-DMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.

Classification with Local Clustering in Imbalanced Data Sets

2011 ◽  
Vol 219-220 ◽  
pp. 151-155 ◽  
Author(s):  
Hua Ji ◽  
Hua Xiang Zhang
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Local Clustering ◽  
Rare Class ◽  
Novel Method ◽  
The Cost

In many real-world domains, learning from imbalanced data sets is always confronted. Since the skewed class distribution brings the challenge for traditional classifiers because of much lower classification accuracy on rare classes, we propose the novel method on classification with local clustering based on the data distribution of the imbalanced data sets to solve this problem. At first, we divide the whole data set into several data groups based on the data distribution. Then we perform local clustering within each group both on the normal class and the disjointed rare class. For rare class, the subsequent over-sampling is employed according to the different rates. At last, we apply support vector machines (SVMS) for classification, by means of the traditional tactic of the cost matrix to enhance the classification accuracies. The experimental results on several UCI data sets show that this method can produces much higher prediction accuracies on the rare class than state-of-art methods.

An Instance Selection Algorithm Based on ReliefF

2019 ◽  
Vol 28 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Zeinab Abbasi ◽  
Mohsen Rahmani
Keyword(s):  
Missing Values ◽  
Imbalanced Data ◽  
Jaccard Index ◽  
Instance Selection ◽  
Data Sets ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Numeric Data

Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algorithm, based on the Jaccard index, the nearest instances of each class are found for each instance. Then, based on the nearest neighbor’s set, the weight of each instance is calculated. Finally, only instances with more weights are selected. This algorithm can reduce data at a specified rate and have the ability to run parallel on the instances. It can work on a variety of data sets with nominal and numeric data with missing values and is also suitable for working with imbalanced data sets. The proposed algorithm tests on three data sets. Results show that the proposed algorithm can reduce the volume of data, without a significant change in classification accuracy of these datasets.

scholarly journals Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

2021 ◽  
Vol 11 (11) ◽  
pp. 4970
Author(s):  
Łukasz Rybak ◽  
Janusz Dudczyk
Keyword(s):  
Learning Algorithm ◽  
Imbalanced Data ◽  
Evaluation Process ◽  
Unbalanced Data ◽  
Data Sets ◽  
Classification Problems ◽  
Mass Model ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Occupancy Detection

The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their geometrical divide. In the previous analyses it was established that the Geometrical Divide (GD) method outperforms the algorithm creating the data particles based on classes by a compound of 1 ÷ 1 cardinality. This occurs in the process of balanced data sets classification, in which class centroids are close to each other and the groups of objects, described by different labels, overlap. The purpose of the article was to examine the efficiency of the Geometrical Divide method in the unbalanced data sets classification, by the example of real case-occupancy detecting. In addition, in the paper, the concept of the Unequal Geometrical Divide (UGD) was developed. The evaluation of approaches was conducted on 26 unbalanced data sets-16 with the features of Moons and Circles data sets and 10 created based on real occupancy data set. In the experiment, the GD method and its unbalanced variant (UGD) as well as the 1CT1P approach, were compared. Each method was combined with three data particle mass determination algorithms-n-Mass Model (n-MM), Stochastic Learning Algorithm (SLA) and Bath-update Algorithm (BLA). k-fold cross validation method, precision, recall, F-measure, and number of used data particles were applied in the evaluation process. Obtained results showed that the methods based on geometrical divide outperform the 1CT1P approach in the imbalanced data sets classification. The article’s conclusion describes the observations and indicates the potential directions of further research and development of methods, which concern creating the data particle through its geometrical divide.

scholarly journals Critical assessment of coiled-coil predictions based on protein structure data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dominic Simm ◽  
Klas Hatje ◽  
Stephan Waack ◽  
Martin Kollmar
Keyword(s):  
Binary Classification ◽  
Coiled Coil ◽  
Imbalanced Data ◽  
Data Bank ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Protein Motifs ◽  
Accuracy And Precision ◽  
Reliable Performance

AbstractCoiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence.

scholarly journals Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Li ◽  
Tian-ge Liang ◽  
Kai-hui Zhang
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Boundary Density ◽  
Cluster Boundary ◽  
Penalty Factor ◽  
Density Clustering ◽  
Scientific Significance ◽  
Density Threshold

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.

scholarly journals SAFETY RISK EVALUATIONS OF DEEP FOUNDATION CONSTRUCTION SCHEMES BASED ON IMBALANCED DATA SETS

2020 ◽  
Vol 26 (4) ◽  
pp. 380-395
Author(s):  
Peisong Gong ◽  
Haixiang Guo ◽  
Yuanyue Huang ◽  
Shengyu Guo
Keyword(s):  
Imbalanced Data ◽  
Classification Algorithm ◽  
Support Vector ◽  
Data Sets ◽  
Foundation Engineering ◽  
Deep Foundation ◽  
Data Set ◽  
Safety Risk ◽  
Imbalanced Data Sets ◽  
Foundation Construction

Safety risk evaluations of deep foundation construction schemes are important to ensure safety. However, the amount of knowledge on these evaluations is large, and the historical data of deep foundation engineering is imbalanced. Some adverse factors influence the quality and efficiency of evaluations using traditional manual evaluation tools. Machine learning guarantees the quality of imbalanced data classifications. In this study, three strategies are proposed to improve the classification accuracy of imbalanced data sets. First, data set information redundancy is reduced using a binary particle swarm optimization algorithm. Then, a classification algorithm is modified using an Adaboost-enhanced support vector machine classifier. Finally, a new classification evaluation standard, namely, the area under the ROC curve, is adopted to ensure the classifier to be impartial to the minority. A transverse comparison experiment using multiple classification algorithms shows that the proposed integrated classification algorithm can overcome difficulties associated with correctly classifying minority samples in imbalanced data sets. The algorithm can also improve construction safety management evaluations, relieve the pressure from the lack of experienced experts accompanying rapid infrastructure construction, and facilitate knowledge reuse in the field of architecture, engineering, and construction.

Imbalanced Data Classification Using Cost-Sensitive Support Vector Machine Based on Information Entropy

2014 ◽  
Vol 989-994 ◽  
pp. 1756-1761 ◽  
Author(s):  
Wei Duan ◽  
Liang Jing ◽  
Xiang Yang Lu
Keyword(s):  
Support Vector Machine ◽  
Information Entropy ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Penalty Factor ◽  
Imbalanced Data Classification

As a supervised classification algorithm, Support Vector Machine (SVM) has an excellent ability in solving small samples, nonlinear and high dimensional classification problems. However, SVM is inefficient for imbalanced data sets classification. Therefore, a cost sensitive SVM (CSSVM) should be designed for imbalanced data sets classification. This paper proposes a method which constructed CSSVM based on information entropy, and in this method the information entropies of different classes of data set are used to determine the values of penalty factor of CSSVM.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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