SVM Tree for Personalized Transductive Learning in Bioinformatics Classification Problems

2014 ◽  
pp. 223-231 ◽  
Author(s):  
Maurizio Fiasché
Keyword(s):  
Classification Problems ◽  
Transductive Learning

MULTI-CLASS TRANSDUCTIVE CLASSIFICATION BASED ON LOCAL LEARNING AND ADJUSTABLE CLASS LABEL REPRESENTATION

2012 ◽  
Vol 26 (06) ◽  
pp. 1250014
Author(s):  
JIA LV ◽  
NAIYANG DENG
Keyword(s):  
Learning Problems ◽  
Local Learning ◽  
Classification Problems ◽  
Class Label ◽  
Transductive Learning ◽  
Good Classification ◽  
Density Relationship ◽  
Representation Method ◽  
Multi Class Classification

Local learning has been successfully applied to transductive classification problems. In this paper, it is generalized to multi-class classification of transductive learning problems owing to its good classification ability. Meanwhile, there is essentially no ordinal meaning in class label of multi-class classification, and it belongs to discrete nominal variable. However, common binary series class label representation has the equal distance from one class to another, and it does not reflect the sparse and density relationship among classes distribution, so a learning and adjustable nominal class label representation method is presented. Experimental results on a set of benchmark multi-class datasets show the superiority of our algorithm.

TRANSDUCTIVE LEARNING FOR SHORT-TEXT CLASSIFICATION PROBLEMS USING LATENT SEMANTIC INDEXING

2005 ◽  
Vol 19 (02) ◽  
pp. 143-163 ◽  
Author(s):  
SARAH ZELIKOVITZ ◽  
FINELLA MARQUEZ
Keyword(s):  
Text Classification ◽  
Latent Semantic Indexing ◽  
Training Data ◽  
Data Sets ◽  
Semantic Indexing ◽  
Classification Problems ◽  
Transductive Learning ◽  
Short Text ◽  
Series Of Experiments ◽  
Value Decomposition

This paper presents work that uses Transductive Latent Semantic Indexing (LSI) for text classification. In addition to relying on labeled training data, we improve classification accuracy by incorporating the set of test examples in the classification process. Rather than performing LSI's singular value decomposition (SVD) process solely on the training data, we instead use an expanded term-by-document matrix that includes both the labeled data as well as any available test examples. We report the performance of LSI on data sets both with and without the inclusion of the test examples, and we show that tailoring the SVD process to the test examples can be even more useful than adding additional training data. This method can be especially useful to combat possible inclusion of unrelated data in the original corpus, and to compensate for limited amounts of data. Additionally, we evaluate the vocabulary of the training and test sets and present the results of a series of experiments to illustrate how the test set is used in an advantageous way.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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