# Hierarchical Concept Learning by Fuzzy Semantic Cells
## 5 Conclusions  
In this paper, we propose a hierarchical concept learning model based on the fuzzy semantic cell. There are two main contributions of this model. Firstly, the model can be  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Synthetic Dataset** & **Forest** & **Pendigits** & **MNIST** & **MIT Face** \\  & _75 as Training_ & _52 as Training_ & **1000 as Training** & **7000 as Training** & **320 as Training** \\  & _67s as Test_ & _471 as Test_ & **9902 as Test** & **63,000 as Test** & **2920 as test** \\ \hline DecisionTree & 96.07 \(\pm\) 1.21 & 79.45 \(\pm\) 2.09 & 88.00 \(\pm\) 0.31 & 76.24 \(\pm\) 0.16 & 53.02 \(\pm\) 8.44 \\ NaiveBayes & **98.81 \(\pm\) 0.00** & 82.59 \(\pm\) 0.00 & 85.41 \(\pm\) 0.00 & 78.65 \(\pm\) 0.00 & 57.53 \(\pm\) 0.00 \\ KNN & 97.93 \(\pm\) 0.00 & **83.86 \(\pm\) 0.00** & 95.58 \(\pm\) 0.00 & 90.63 \(\pm\) 0.00 & **90.34 \(\pm\) 0.00** \\ SVM & 97.93 \(\pm\) 0.00 & 82.80 \(\pm\) 0.00 & 95.35 \(\pm\) 0.71 & 90.78 \(\pm\) 0.00 & 71.76 \(\pm\) 0.02 \\ AdaBoost & 96.74 \(\pm\) 0.00 & 77.49 \(\pm\) 1.73 & 69.01 \(\pm\) 0.00 & 72.12 \(\pm\) 0.00 & 60.96 \(\pm\) 0.74 \\ Neural Network & 96.89 \(\pm\) 0.31 & 31.66 \(\pm\) 14.94 & **98.29 \(\pm\) 0.00** & **92.37 \(\pm\) 0.11** & 75.42 \(\pm\) 4.95 \\
**FSC (ours)** & **98.50 \(\pm\) 0.04** & **82.93 \(\pm\) 1.66** & **96.27 \(\pm\) 0.14** & **90.88 \(\pm\) 0.43** & **91.32 \(\pm\) 0.59** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The classification accuracy in percentage (mean \(\pm\) standard deviation) of test set on five datasets (only 10% as training set). All results are the summary of repeated six runs. The best and second best results are in bold.  
used to model and learn abstract concepts in a supervised manner. Secondly, this model can be used to achieve a good learning effect with a small amount of data (even only 10%). According to the experimental results, our proposed method is comparable with typical techniques in terms of robustness and generalization ability under the circumstances of limited training data.  
In the future, we will improve the objective function to make it more modeling capable while also looking for a better strategy to initialize parameters and to solve non-convex problems. We will concentrate on combining the proposed concept learning mechanism with other common models and investigating a feasible method of expanding more complex abstract concepts to improve concept learning performance.  
Conceptualization, Y.T. and L.Z.; software, L.Z. and W.L.; writing--original draft preparation, L.Z. and W.L.; writing--review and editing, Y.T. and L.Z.; supervision, Y.T. All authors have read and agreed to the published version of the manuscript.  
Projects supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China (No. 2018 AAA0100703) and the National Natural Science Foundation of China (No. 61773336).  
Not applicable.  
We would like to extend our gratitude to data providers.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FSC Fuzzy Semantic Cell  
KNN K-Nearest Neighbor  
DT Decision Tree  
SVM Support Vector Machine