Does Deep Learning Work Well for Categorical Datasets with Mainly Nominal Attributes?

Yoichi Hayashi

doi:10.3390/electronics9111966

Abstract

Given the complexity of real-world datasets, it is difficult to present data structures using existing deep learning (DL) models. Most research to date has concentrated on datasets with only one type of attribute: categorical or numerical. Categorical data are common in datasets such as the German (-categorical) credit scoring dataset, which contains numerical, ordinal, and nominal attributes. The heterogeneous structure of this dataset makes very high accuracy difficult to achieve. DL-based methods have achieved high accuracy (99.68%) for the Wisconsin Breast Cancer Dataset, whereas DL-inspired methods have achieved high accuracy (97.39%) for the Australian credit dataset. However, to our knowledge, no such method has been proposed to classify the German credit dataset. This study aimed to provide new insights into the reasons why DL-based and DL-inspired classifiers do not work well for categorical datasets, mainly consisting of nominal attributes. We also discuss the problems associated with using nominal attributes to design high-performance classifiers. Considering the expanded utility of DL, this study's findings should aid in the development of a new type of DL that can handle categorical datasets consisting of mainly nominal attributes, which are commonly used in risk evaluation, finance, banking, and marketing.

Highlights

IntroductionWolpert [4,5] described what has come to be known as the no free lunch (NFL) theorem, which implies that all learning algorithms perform well when averaged over all possible datasets
Considering the expanded utility of deep learning (DL), this study's findings should aid in the development of a new type of DL that can handle categorical datasets consisting of mainly nominal attributes, which are commonly used in risk evaluation, finance, banking, and marketing
We investigated the Wisconsin Breast Cancer Dataset (WBCD) using Zhou and Feng’s codes. “----“means that the literature provided no information about the area under the receiver operating characteristic curve (AUC-ROC); TS ACC: accuracy for test dataset; SVM: support vector machine; 10CV: 10-fold cross-validation; 1D FCLF-convolutional neural networks (CNNs): one-dimensional fully-connected layer first convolutional neural network

Summary

Introduction

Wolpert [4,5] described what has come to be known as the no free lunch (NFL) theorem, which implies that all learning algorithms perform well when averaged over all possible datasets. This counterintuitive concept thereby suggests the infeasibility of finding a general, highly predictive algorithm. Gŏmez and Rojas [6] subsequently empirically investigated the effects of the NFL theorem on several popular machine learning (ML) classification techniques using real-world datasets.

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