# Complement-Class Harmonized Naive Bayes Classifier
## 4 Experimental Setup and Results
### Comparison to State-of-the-Art (General Datasets)  
In this section, the performance of the proposed method is compared with attribute weighting NB classifiers' wrapper-based methods (WANBIACLL, CAWNBLBL, and CAVWNBLL), filter-based methods (CAVWMI), fine tuning naive base (FTNB), combined filter-based and fine-tuning method (FTANB), and the original NB algorithm. The related work methods and their abbreviations are listed in Table 1.  
Comprehensive experiments were conducted on 41 benchmark datasets obtained from the UCI repository [50]. Most datasets were collected from real-world problems, which represent a wide range of domains and data characteristics. The number of attributes/classes of these datasets varies, and hence, these datasets are diverse and challenging. Table 2 shows the properties of these data sets. Table 3 shows the detailed classification accuracy obtained by averaging the results from stratified 10-fold cross-validation. The results of CAVWNBCLL, CAWNBCLL, and WANBIACHL were obtained from [34]. The results of CAVWMI and FTANB were obtained from [30,40], respectively. The overall classification average result and the Win/Tie/Lose (W/T/L) values are summarized at the bottom of the table in addition to the other statistics. Each entry's W/T/L in the table implies that the competitor wins on W datasets, ties on T datasets, and loses on L datasets compared with the proposed method. The field marked with \(\bullet\) and \(\odot\) implies that the classification accuracy of CHNB has statistically significant upgrades or degrades, respectively, compared with the competitor algorithm. We employed a paired tow-tailed \(t\)-test with a \(p=0.05\) significance level. \begin{table}
\begin{tabular}{l l l l l} \hline \hline WANBIACHL & Attribute weighting NB with gradient based optimization on conditional log likelihood (CLL) [32] \\ \hline CAWNBGL & Class-specific Attribute weighting NB with gradient based optimization on (CLL) [33] \\ \hline CAWWNBGL & Class-specific Attribute value weighting NB with gradient based optimization on (CLL) [34] \\ \hline CAWWMI & Filter method, correlation-based attribute value weighting measured by Mutual Information (MI) [30] \\ \hline FTNB & Fine tuning naïve Bayes [39] \\ \hline FTANB & Initial attribute weighted based on CAWWMI, then fine-tuned with FTNB algorithm [40] \\ \hline NB & Base line multinomial NB \\ \hline CHNB & Complement-class fine tuning naïve Bayes (ours) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the competitors’ NB classifiers. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Instance** & **Attributes** & **Classes** & **Missing Values** \\ \hline Anneal & 898 & 39 & 6 & Y \\ AnnealOrig & 898 & 39 & 6 & Y \\ Audiology & 226 & 70 & 24 & Y \\ Autos & 205 & 26 & 7 & Y \\ Breast-cancer & 286 & 10 & 2 & Y \\ Breast-w & 699 & 10 & 2 & Y \\ Car & 1728 & 7 & 4 & N \\ Colic & 368 & 23 & 2 & Y \\ Colic.COIG & 368 & 28 & 2 & Y \\ Credit-a & 690 & 16 & 2 & Y \\ Credit-g & 1000 & 21 & 2 & N \\ Cylinderbands & 540 & 41 & 2 & Y \\ Diabetes & 768 & 9 & 2 & N \\ Ecoli & 336 & 8 & 8 & N \\ Glass & 214 & 10 & 7 & N \\ Heart-c & 303 & 14 & 5 & Y \\ Heart-h & 294 & 14 & 5 & Y \\ Heart-statog & 270 & 14 & 2 & N \\ Hepatitis & 155 & 20 & 2 & Y \\ Hypothyroid & 3772 & 30 & 4 & Y \\ Ionosphere & 351 & 35 & 2 & N \\ Iris & 150 & 5 & 3 & N \\ KR-vs-KP & 3196 & 37 & 2 & N \\ Labor & 57 & 17 & 2 & Y \\ Letter & 20,000 & 17 & 26 & N \\ Lymphh & 148 & 19 & 4 & N \\ Mushroom & 8124 & 23 & 2 & Y \\ Optdigits & 5620 & 63 & 10 & N \\ Page.blocks & 5473 & 11 & 5 & N \\ Pendigits & 10,992 & 17 & 10 & N \\ Primary-tumor & 339 & 18 & 21 & Y \\ Segment & 2310 & 20 & 7 & N \\ Sick & 3772 & 30 & 2 & Y \\ Sonar & 208 & 61 & 2 & N \\ Soybean & 683 & 36 & 19 & Y \\ Splice & 3190 & 62 & 3 & N \\ Vehicle & 846 & 19 & 4 & N \\ Vote & 435 & 17 & 2 & Y \\ Vowel & 990 & 14 & 11 & N \\ Waveform & 1000 & 41 & 3 & N \\ Zoo & 101 & 18 & 7 & N \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UCI general dataset description.