Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Wu Chen ◽  
Yong Yu ◽  
Keke Gai ◽  
Jiamou Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Interaction Mechanism ◽  
Training Model ◽  
Edge Computing ◽  
Learning Techniques ◽  
Multi Agent ◽  
Real World Datasets ◽  
Entire Dataset ◽  
Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

scholarly journals Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network

2019 ◽  
Author(s):  
Zheng Liu ◽  
Yu Xing ◽  
Fangzhao Wu ◽  
Mingxiao An ◽  
Xing Xie
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Recommendation Systems ◽  
User Preference ◽  
High Fidelity ◽  
Learning Techniques ◽  
Entire History ◽  
Real World Datasets ◽  
User History ◽  
Candidate Item

Deep learning techniques have been widely applied to modern recommendation systems, bringing in flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent algorithms tend to generate user representation at runtime, where user's historical behaviors are attentively summarized w.r.t. the presented candidate item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of the repetitive access to user's entire history. In this work, a novel user representation framework, Hi-Fi Ark, is proposed. With Hi-Fi Ark, user history is summarized into highly compact and complementary vectors in the offline stage, known as archives. Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated on three real-world datasets, where remarkable and consistent improvements are made over a variety of well-recognized baseline methods.

scholarly journals Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (Preprint)

2017 ◽  
Author(s):  
Miran Kim ◽  
Yongsoo Song ◽  
Shuang Wang ◽  
Yuhou Xia ◽  
Xiaoqian Jiang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Real World ◽  
Homomorphic Encryption ◽  
Training Model ◽  
Logistic Function ◽  
Data Outsourcing ◽  
Sensitive Data ◽  
Real World Datasets ◽  
Linear Means

BACKGROUND Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. OBJECTIVE The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). METHODS We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques. RESULTS Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. CONCLUSIONS We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.

scholarly journals FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION

Transmisi ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 102-106
Author(s):  
Farrikh Alzami ◽  
Aries Jehan Tamamy ◽  
Ricardus Anggi Pramunendar ◽  
Zaenal Arifin
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Epileptic Seizure ◽  
Learning Approach ◽  
Fusion Rules ◽  
Weak Learner ◽  
Real World Datasets ◽  
Seizure Classification ◽  
Bagging Ensemble

The ensemble learning approach, especially in classification, has been widely carried out and is successful in many scopes, but unfortunately not many ensemble approaches are used for the detection and classification of epilepsy in biomedical terms. Compared to using a simple bagging ensemble framework, we propose a fusion bagging-based ensemble framework (FBEF) that uses 3 weak learners in each oracle, using fusion rules, a weak learner will give results as predictors of the oracle. All oracle predictors will be included in the trust factor to get a better prediction and classification. Compared to traditional Ensemble bagging and single learner type Ensemble bagging, our framework outperforms similar research in relation to the epileptic seizure classification as 98.11±0.68 and several real-world datasets

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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