Keisuke Goto

TCS Journal 2024 Journal Article

Linear time online algorithms for constructing linear-size suffix trie

• Diptarama Hendrian
• Takuya Takagi
• Shunsuke Inenaga
• Keisuke Goto
• Mitsuru Funakoshi

AAAI Conference 2022 Conference Paper

Explainable and Local Correction of Classification Models Using Decision Trees

• Hirofumi Suzuki
• Hiroaki Iwashita
• Takuya Takagi
• Keisuke Goto
• Yuta Fujishige
• Satoshi Hara

In practical machine learning, models are frequently updated, or corrected, to adapt to new datasets. In this study, we pose two challenges to model correction. First, the effects of corrections to the end-users need to be described explicitly, similar to standard software where the corrections are described as release notes. Second, the amount of corrections need to be small so that the corrected models perform similarly to the old models. In this study, we propose the first model correction method for classification models that resolves these two challenges. Our idea is to use an additional decision tree to correct the output of the old models. Thanks to the explainability of decision trees, the corrections are describable to the end-users, which resolves the first challenge. We resolve the second challenge by incorporating the amount of corrections when training the additional decision tree so that the effects of corrections to be small. Experiments on real data confirm the effectiveness of the proposed method compared to existing correction methods.

TCS Journal 2022 Journal Article

In-place initializable arrays

• Takashi Katoh
• Keisuke Goto

AAAI Conference 2018 Conference Paper

Learning Multi-Way Relations via Tensor Decomposition With Neural Networks

• Koji Maruhashi
• Masaru Todoriki
• Takuya Ohwa
• Keisuke Goto
• Yu Hasegawa
• Hiroya Inakoshi
• Hirokazu Anai

How can we classify multi-way data such as network traffic logs with multi-way relations between source IPs, destination IPs, and ports? Multi-way data can be represented as a tensor, and there have been several studies on classification of tensors to date. One critical issue in the classification of multi-way relations is how to extract important features for classification when objects in different multi-way data, i. e. , in different tensors, are not necessarily in correspondence. In such situations, we aim to extract features that do not depend on how we allocate indices to an object such as a specific source IP; we are interested in only the structures of the multi-way relations. However, this issue has not been considered in previous studies on classification of multi-way data. We propose a novel method which can learn and classify multi-way data using neural networks. Our method leverages a novel type of tensor decomposition that utilizes a target core tensor expressing the important features whose indices are independent of those of the multi-way data. The target core tensor guides the tensor decomposition into more effective results and is optimized in a supervised manner. Our experiments on three different domains show that our method is highly accurate, especially on higher order data. It also enables us to interpret the classification results along with the matrices calculated with the novel tensor decomposition.