What is JSAI (Japan Society for Artificial Intelligence)?

The AI paper introduced in this article is titled "Development of general-purpose CNN deep learning method using feature graph". This paper was presented at JSAI 2018 (Japanese Society for Artificial Intelligence 2018).
JSAI is an AI conference held in Japan, and I briefly introduced it in a previous tech blog.

Papers presented at AI conferences are a source of information on AI trends and are extremely valuable for learning about new methods. Be sure to check out our previous tech blog!
Tech Blog: Learning the Cutting Edge of AI from Papers In addition, two of our researchers will participate in this year's JSAI2019. We are planning to publish the state of JSAI on the tech blog, so please look forward to it too!

The AI paper introduced in this article is titled "Development of general-purpose CNN deep learning method using feature graph". This paper was presented at JSAI 2018 (Japanese Society for Artificial Intelligence 2018).
JSAI is an AI conference held in Japan, and I briefly introduced it in a previous tech blog.

Papers presented at AI conferences are a source of information on AI trends and are extremely valuable for learning about new methods. Be sure to check out our previous tech blog!
Tech Blog: Learning the cutting edge of AI from papers
In addition, two of our researchers will participate in JSAI2019 this year. We are planning to publish the state of JSAI on the tech blog, so please look forward to it too!

今回ご紹介する論文のテーマは「CNNの応用手法」

Do you know about CNN (Convolutional Neural Network), which is the basis of GraphCNN?
CNN is a neural network with deep layers and is often used in the image field.
To be useful with today's masses of data, we need to understand large and complex data sets. CNN is attracting attention as an effective means of doing so, which is the fundamental reason for picking up this AI paper.

CNN is difficult to adapt to non-image data

As I mentioned earlier, CNN is a method that has been widely used mainly in the image field.
This is because the order of elements in image data has meaning, and CNN analyzes the order of elements that has that meaning as a feature.
So what about non-image data like csv? For non-image data, the meaning of the data does not change even if the column order is changed. Therefore, it was not possible to adapt to data with changing graph structure.

Correlation Graph CNN (C-Graph CNN)

However, recently, a method that overturns conventional thinking has been reported. "GraphCNN (Graph Convolution Neural Network)" was born from the reversal of the idea that "CNN can be used if the order of non-image data is also meaningful!"

As the name suggests, it is a neural network that performs a convolution operation on the graph structure. The "graph structure" mentioned here is not something like a pie chart, but a network structure like the one shown in the figure below.

For example, the method "C-GraphCNN" is also a kind of GraphCNN.
C-GraphCNN is a technique that uses "correlation coefficient" to give meaning to the order of non-image data and adapt CNN.
Since the correlation coefficient is an index that looks at the relationship between adjacent variables, it cannot be rearranged by nature. In other words, C-GraphCNN is a method using "sequentially meaningful" correlation coefficients.

C-GraphCNN seems groundbreaking when explained in this way, but there are also issues.
The problem is that the correlation coefficient does not take into account the actual distance between the data, so it is a suboptimal way to express the relationship between the data.

■ Kernel Graph CNN

Uses an algorithm called Gaussian kernel to create the distance matrix

■ DBSCAN Graph CNN

Use a clustering algorithm called DBSCAN to create the distance matrix

D-GraphCNN has high analysis accuracy

In the paper, experiments are conducted using two datasets to confirm the usefulness of D-GraphCNN. In this blog, I will introduce one of the experiments as an example.
Below are the results of an experiment on a dataset called 'The Boston Housing Dataset', which deals with the regression problem of predicting Boston housing prices from 12 pieces of information about property demographics.

The closer the coefficient of determination on the vertical axis of the graph is to 1, the higher the precision of the analysis.
From the graph of the experimental results, we can certainly see the usefulness of GraphCNN (C-GraphCNN / Kernel-GraphCNN / DBSCAN-GraphCNN).
D-GraphCNN (Kernel-GraphCNN / DBSCAN-GraphCNN) in particular has high accuracy.

Applying GraphCNN to image data

In the paper, we use the MNIST dataset to challenge the image classification problem. MNIST is an image dataset containing 60,000 images of handwritten digits and 10,000 test images.
The figure below shows the results of actually using the nine-layer CNN, C-GraphCNN, and D-GraphCNN methods for learning image data. Even for image data, the accuracy rate of GraphCNN exceeds 90%, confirming its high performance.

Can also be used for reinforcement learning applications

Similarly, for reinforcement learning, we replace the neural network used for reinforcement learning with GraphCNN and check the performance.
The content of the experiment is to see the game score transition called CartPole in a simulator environment using CartPole of Open AI gym.
The figure below shows the transition of the score, but the left side is the result of the neural network and the right side is the result of C-GraphCNN.

From the graph, we can see that C-GraphCNN reaches the highest point faster and maintains the highest point more times. From this, GraphCNN is considered to be effective, although there is a problem that the score difference of the accuracy rate is large.