Opinion fraud detection via neural autoencoder decision forest

Dong, Manqing; Yao, Lina; Wang, Xianzhi; Benatallah, Boualem; Huang, Chaoran; Ning, Xiaodong

doi:10.1016/j.patrec.2018.07.013

Cited by 48 publications

(30 citation statements)

References 26 publications

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“…Therefore, it is desirable to build a fraud detection model to identify and weed out fake reviews. Dong et al (2018) [145] present an autoencoder and random forest, where a stochastic decision tree model fine tunes the parameters. Extensive experiments were conducted on a large Amazon review dataset.…”

Section: A Fraud Detection In Financial Servicesmentioning

confidence: 99%

A review of deep learning with special emphasis on architectures, applications and recent trends

Sengupta

Basak

Saikia

et al. 2020

Knowledge-Based Systems

304

143

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Deep learning has solved a problem that as little as five years ago was thought by many to be intractable -the automatic recognition of patterns in data; and it can do so with an accuracy that often surpasses that of human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of deep learning increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular Deep Learning model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer artificial neural networks that comprise deep learning. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to use deep learning in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.

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Section: A Fraud Detection In Financial Servicesmentioning

confidence: 99%

A review of deep learning with special emphasis on architectures, applications and recent trends

Sengupta

Basak

Saikia

et al. 2020

Knowledge-Based Systems

304

143

View full text Add to dashboard Cite

show abstract

“…Kontschieder et al [24] introduced a stochastic, differentiable, backpropagation compatible version of decision forest, guiding representation learning in the lower layers of deep convolutional networks. Based on this, Dong et al [25] combined an autoencoder with it into a joint model to detect opinion fraud. In this work, Dong et al first employed statistical analysis to define a list of quality measures for online reviews and then used the autoencoder to generate the hidden representations of these features.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Smoking Event Detection Using a Variational Autoencoder and Neural Decision Forest

Fan

Gao

2020

IEEE Access

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Smoking is associated with cancer, cardiovascular disease and premature death and can cause severe fire hazards. To assist with smoking cessation, this paper presents a wireless body area networkbased system consisting of two off-the-shelf devices, one smartphone and one smartwatch, to detect smoking events by mining the inertial sensor data from both devices. In the system, an end-to-end trainable unified model is implemented by combining a variational autoencoder with a random forest to classify the collected data into smoking and nonsmoking after data preprocessing. The variational autoencoder is adopted to learn the feature representation and deal with the class imbalance problem, and the stochastic decision forest is adopted to guide the global optimization of the parameter learning process. Extensive validation of our scheme is performed on a large dataset we collected, and our scheme yields quite promising results in terms of accuracy and efficiency.

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“…Supervised learning techniques that have been used for spam review detection so far are; Rule based classification [5,10], Unified model [2], Logistic Regression [4,11,12], Knearest neighbor (KNN) [4], Random Forest [4,[13][14][15], Decision Trees [16,17], Gradient Decent [4,10], Genetic Algorithm [18], Conceptual Model [19], Time Series [20], Neural Network [21], Deep Neural Network [22], Multinomial Naïve Bayes [9,11,13], N-Gram [13], Hybrid Learning Approach (Active and supervised learning) [23], RNN, CNN [24], and Multilayer Perceptron Model (MLP) [4,24], Unsupervised learning is a category of machine learning that work on the unlabeled datasets. Many unsupervised learning techniques have been used in spam detection which are: Natural Language Processing [6,9][58] Markov Network [25], Neural Auto-encoder Decision Forest [16]¸ and PU Learning [26]. Other than these supervised and unsupervised learning techniques, there are many other techniques that have been used for spam detection such as Fuzzy Logic [27], Heterogeneous Information Network [28], Hadoop [29], Text Mining [30], Sentiment Analysis [31][32][33][34]<...>…”

Section: Figure 1 Types Of Spammentioning

confidence: 99%

“…However, Jindal and Liu identified that many spam reviews were written in a way that it looks authentic. Hence, they determined that using duplication feature to differentiate legitimate reviews and spam reviews is not suited for creating label datasets [16].…”

Section: Figure 1 Types Of Spammentioning

confidence: 99%

Spam Review Detection:A Systematic Literature Review

Farooq¹

2020

Preprint

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In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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Opinion fraud detection via neural autoencoder decision forest

Cited by 48 publications

References 26 publications

A review of deep learning with special emphasis on architectures, applications and recent trends

A review of deep learning with special emphasis on architectures, applications and recent trends

A New Approach for Smoking Event Detection Using a Variational Autoencoder and Neural Decision Forest

Spam Review Detection:A Systematic Literature Review

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