scholarly journals Video Genre Classification Using Weighted Kernel Logistic Regression

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ahmed A. M. Hamed ◽  
Renfa Li ◽  
Zhang Xiaoming ◽  
Cheng Xu
Keyword(s):  
Logistic Regression ◽  
Video Data ◽  
Semantic Gap ◽  
Good Representation ◽  
Classification Problems ◽  
Computational Tools ◽  
Kernel Logistic Regression ◽  
Genre Classification ◽  
Weighted Kernel ◽  
Multiclass Classification Problems

Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results.

scholarly journals Predictingβ-Turns in Protein Using Kernel Logistic Regression

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Murtada Khalafallah Elbashir ◽  
Yu Sheng ◽  
Jianxin Wang ◽  
FangXiang Wu ◽  
Min Li
Keyword(s):  
Logistic Regression ◽  
Protein Structures ◽  
Structure Type ◽  
Support Vector ◽  
Classification Problems ◽  
Structure Information ◽  
Kernel Logistic Regression ◽  
Scoring Matrices ◽  
The Right ◽  
And Function

Aβ-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located inβ-turns. It is very important to develope an accurate and efficient method forβ-turns prediction. Most of the current successfulβ-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found inβ-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparseβ-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achievedQtotalof 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs inβ-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.

scholarly journals A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

2021 ◽  
Author(s):  
Ferdinand Bollwein ◽  
Stephan Westphal
Keyword(s):  
Decision Tree ◽  
Feature Space ◽  
Classification Problems ◽  
Decision Tree Induction ◽  
Single Attribute ◽  
Global Optimal ◽  
The Individual ◽  
Tree Building ◽  
Very High ◽  
Multiclass Classification Problems

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

Computational tools for exact conditional logistic regression

2001 ◽  
Vol 20 (17-18) ◽  
pp. 2723-2739 ◽  
Author(s):  
Chris Corcoran ◽  
Cyrus Mehta ◽  
Nitin Patel ◽  
Pralay Senchaudhuri
Keyword(s):  
Logistic Regression ◽  
Conditional Logistic Regression ◽  
Computational Tools

Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

2021 ◽  
Vol 17 (3) ◽  
pp. 50-62
Author(s):  
Ayodeji Samuel Makinde ◽  
Abayomi O. Agbeyangi ◽  
Wilson Nwankwo
Keyword(s):  
Logistic Regression ◽  
Telecommunication Networks ◽  
Mobile Service ◽  
Personal Choice ◽  
Kernel Logistic Regression ◽  
Service Efficiency ◽  
Number Portability ◽  
Prediction Techniques ◽  
The Cost ◽  
Potential Customers

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.

Computer Implementation of Genetic Programming

2018 ◽  
pp. 132-182
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Programming Language ◽  
Object Oriented Programming ◽  
Computer Implementation ◽  
Classification Problems ◽  
Development Environment ◽  
Terminal Set ◽  
C Programming ◽  
Multiclass Classification Problems

This chapter presents the computer implementation of the tree-based genetic programming in C# programming language. Since C# is a common object-oriented programming language, with little modification the source code presented in the chapter can be easily transformed into Java or C++ programming languages. The chapter covers all aspects of the implementation: node, chromosome, population, function set, and terminal set class implementations. The chapter is carefully structured, so at the end of the chapter fully working GP computer program will be implemented which can solve regression and multiclass classification problems. The reader should not worry about specific operating system, or development environment, since all code implementations are based on cross-OS and open source integrated development environment visual studio code which can run on Windows, Mac, or Linux.

scholarly journals A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

mBio ◽  
2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Begüm D. Topçuoğlu ◽  
Nicholas A. Lesniak ◽  
Mack T. Ruffin ◽  
Jenna Wiens ◽  
Patrick D. Schloss
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Sequence Data ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Model Complexity ◽  
Support Vector ◽  
Classification Problems ◽  
Microbial Biomarkers

ABSTRACT Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

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