Abstract Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
In this work, we propose to integrate neural networks with neural random forest, which is a differentiable model that could be fused into deep learning methods. And there have been already several attempts on leveraging the benefits of deep learning methods and random forest. One of the most important work in this area is the studies that cast the neural random forest as the structure of neural networks. The first few attempts started in the 1990s when Sethi @cite_4 proposed entropy net, which encoded decision trees into neural networks. Welbl et. al @cite_7 further proposed that any decision trees can be represented as two-layer Convolutional Neural Networks (CNN). Since the classical random forest cannot conduct back-propagation, other researchers tried to design differentiable decision tree methods, which can be traced from Montillo's work @cite_0 . They investigated the use of sigmoidal functions for the task of differentiable information gain maximization. In 's work @cite_6 , they introduce a stochastic, differentiable, back propagation compatible version of decision trees, guiding the representation learning in lower layers of deep convolutional networks. Different from their work, we develop a hybrid model by combining neural decision forest with lightweight autoencoder to detect fraud opinion.
@cite_0 @cite_4 @cite_6 @cite_7
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Decision forests can be thought of as a flexible optimization toolbox with many avenues to alter or recombine the underlying architectural components and improve recognition accuracy and efficiency. In this chapter, we present two fundamental approaches for re-architecting decision forests that yield higher prediction accuracy and shortened decision time. How the mapping of decision trees into a multilayer neural network structure can be exploited for the systematic design of a class of layered neural networks, called entropy nets (which have far fewer connections), is shown. Several important issues such as the automatic tree generation, incorporation of the incremental learning, and the generalization of knowledge acquired during the tree design phase are discussed. A two-step methodology for designing entropy networks is presented. The methodology specifies the number of neurons needed in each layer, along with the desired output, thereby leading to a faster progressive training procedure that allows each layer to be trained separately. Two examples are presented to show the success of neural network design through decision-tree mapping. > We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner. To combine these two worlds, we introduce a stochastic and differentiable decision tree model, which steers the representation learning usually conducted in the initial layers of a (deep) convolutional network. Our model differs from conventional deep networks because a decision forest provides the final predictions and it differs from conventional decision forests since we propose a principled, joint and global optimization of split and leaf node parameters. We show experimental results on benchmark machine learning datasets like MNIST and ImageNet and find on-par or superior results when compared to state-of-the-art deep models. Most remarkably, we obtain Top5-Errors of only 7.84 6.38 on ImageNet validation data when integrating our forests in a single-crop, single seven model GoogLeNet architecture, respectively. Thus, even without any form of training data set augmentation we are improving on the 6.67 error obtained by the best GoogLeNet architecture (7 models, 144 crops). While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the majority of the UCI datasets used for benchmarking.