Yosuke Motohashi’s research while affiliated with Strategy& and other places

The lack of data-scientist is big problem in the world. For solving this problem, we develop “Heterogeneous Mixture Learning” technology which automates trial-and-error of data analysis. And also we make many predictive analytics solution using this technology. In this paper, we describe system architecture and analysis method of predictive analytics solution, and also describe problems during operating predictive analytics solution using machine learning.

Model interpretability has been recognized to play a key role in practical data mining. Interpretable models provide significant insights on data and model behaviors and may convince end-users to employ certain models. In return for these advantages, however, there is generally a sacrifice in accuracy, i.e., flexibility of model representation (e.g., linear, rule-based, etc.) and model complexity needs to be restricted in order for users to be able to understand the results. This paper proposes oblique treed sparse additive models (OT-SpAMs). Our main focus is on developing a model which sacrifices a certain degree of interpretability for accuracy but achieves entirely sufficient accuracy with such fully non-linear models as kernel support vector machines (SVMs). OT-SpAMs are instances of region-specific predictive models. They divide feature spaces into regions with sparse oblique tree splitting and assign local sparse additive experts to individual regions. In order to maintain OT-SpAM interpretability, we have to keep the overall model structure simple, and this produces simultaneous model selection issues for sparse oblique region structures and sparse local experts. We address this problem by extending factorized asymptotic Bayesian inference. We demonstrate, on simulation, benchmark, and real world datasets that, in terms of accuracy, OT-SpAMs outperform state-of-the-art interpretable models and perform competitively with kernel SVMs, while still providing results that are highly understandable.

Most time-varying data in our daily life is multi-variate. Moreover, most of such time-varying data contains both numeric and categorical values. It is often meaningful to visualize both of them as they are often correlated. We aim to visualize every value in such time-varying data in a single display space so that we can discover interesting relationships among the values of the time-varying data. This paper presents a heat map-based time-varying data visualization technique which displays both numeric and categorical values in a single display space. The technique assigns time to the horizontal axis of the display space, and vertically arranges the series of colored belts corresponding to the time-sequence values. It generates one belt for a numeric value, and multiple belts for a categorical value. It clusters the belts according to the similarity of color sequences, and re-arranges the belts based on the clustering result. This paper shows an example of the visualization result applying a time-varying multi-variate marketing dataset.