A Semi-supervised Learning Approach for High Dimensional Android Malware Classification

2021 ◽  
pp. 20-31
Author(s):  
Qiao Shang ◽  
Ni Li ◽  
Qi Qi ◽  
Xiao-Wei Lin
Keyword(s):  
Supervised Learning ◽  
High Dimensional ◽  
Learning Approach ◽  
Android Malware ◽  
Malware Classification

An Optimized Semi-Supervised Learning Approach for High Dimensional Datasets

2018 ◽  
pp. 294-321
Author(s):  
Nesma Settouti ◽  
Mostafa El Habib Daho ◽  
Mohammed El Amine Bechar ◽  
Mohammed Amine Chikh
Keyword(s):  
Supervised Learning ◽  
High Dimensional ◽  
Diagnostic Process ◽  
Importance Measure ◽  
Learning Approach ◽  
Learning From Data ◽  
Selection Strategies ◽  
Medical Diagnostic ◽  
Small Set ◽  
High Dimensional Datasets

The semi-supervised learning is one of the most interesting fields for research developments in the machine learning domain beyond the scope of supervised learning from data. Medical diagnostic process works mostly in supervised mode, but in reality, we are in the presence of a large amount of unlabeled samples and a small set of labeled examples characterized by thousands of features. This problem is known under the term “the curse of dimensionality”. In this study, we propose, as solution, a new approach in semi-supervised learning that we would call Optim Co-forest. The Optim Co-forest algorithm combines the re-sampling data approach (Bagging Breiman, 1996) with two selection strategies. The first one involves selecting random subset of parameters to construct the ensemble of classifiers following the principle of Co-forest (Li & Zhou, 2007). The second strategy is an extension of the importance measure of Random Forest (RF; Breiman, 2001). Experiments on high dimensional datasets confirm the power of the adopted selection strategies in the scalability of our method.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals Fingerprinting Mobile Devices Using Personalized Configurations

2016 ◽  
Vol 2016 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Andreas Kurtz ◽  
Hugo Gascon ◽  
Tobias Becker ◽  
Konrad Rieck ◽  
Felix Freiling
Keyword(s):  
Supervised Learning ◽  
Mobile Devices ◽  
Real World ◽  
Third Party ◽  
Learning Approach ◽  
Total Accuracy ◽  
Over Time

Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time

Household Profile Identification for Behavioral Demand Response: A Semi-supervised Learning Approach Using Smart Meter Data

Energy ◽  
2021 ◽  
pp. 121728
Author(s):  
Fei Wang ◽  
Xiaoxing Lu ◽  
Xiqiang Chang ◽  
Xin Cao ◽  
Siqing Yan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Demand Response ◽  
Learning Approach ◽  
Smart Meter
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