# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 2 Related Work
### Adaption Algorithm  
The second category of ML approaches involves procedure adaptation methods. These ML adaptation methods go beyond traditional feature selection techniques and encompass both classification and optimization approaches. In these methods, features are evaluated using multivariable scores or other measures, enabling ML to assess the significance and relevance of each feature in various ways [34; 35]. This selection of ML features aims to identify the optimal subset of features, considering their importance and consistency. One specific approach is the evaluation of Relief characteristics, which is based on detecting variations in feature values among the most similar pairs of instances. For instance, when a variation in functionality is observed between similar pairs, the number of relevant characteristics decreases (a 'hit'). Conversely, in cases where dissimilar class values are encountered within a comparative instance pair, the feature scoring increases (a'miss') if feature differences are detected. Relief-based methods encompass various adaptations for multi-label classification. One such adaptation is the submission of Relief for multi-label classification based on previously adjusted probability estimates [36]. Another modification involves considering \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description of Algorithms** \\ \hline IPF & Interior Function of Penalty \\ \hline FIMF & Foreword Sequence + Label Combination Limited Mutual Information \\  & Interaction Sequence \\ \hline \multirow{2}{*}{MAMFS} & Sequential Forward Selection + High-Order Label Combination + \\  & Mutual Information \\ \hline MDMR & One by One + Mutual Information Selecting Forward Sequences \\ \hline MFNMI & n Forward Sequence Selection + Mutual Information Determined Locally \\ \hline MIFS & Optimization Alternative \\ \hline PSO-MMI & Particle Swarm Optimization \\ \hline mRMR & One by One + Mutual Information + Next Sequence \\ \hline PMU & Forward Sequence + Label Combination Second Order + Mutual Information \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of transformation techniques. the closest instances with the same labels but different values [37]. In [38], a method was introduced that incorporates a Hamming distance-dependent differential function. Algorithm adaptation approaches, as opposed to problem transformation approaches, which act as wrappers around standard ML methods, modify existing ML techniques to handle multi-label classification problems. BR-K-Nearest Neighbors (BRkNN) [39] is a hybrid technique incorporating approaches for producing BRkNN from both ML and kNN. The author of [40] used MLkNN to assess the appropriate set of labels for this model's inclusion in the model evaluation based on the maximization of a testable prediction. Neural models were employed to identify which label collection corresponded to a specific study instance. The NN model is another ML model. Ref. [41] suggested adjusting a fluid, adaptive resonance map for the NN to make the technology presented suitable for the classification of the machine. Mutual information is a frequently used measure in multi-label learning. Two techniques commonly employed to calculate this mutual information are Label Combination (LC) and One by One (OBO). In the case of OBO, it sequentially evaluates each label, calculating the sum of individual scores between each feature and each label. OBO has been combined with sequential forward selection, as seen in [12]. On the other hand, Conditional Mutual Information (CM) considers all labels simultaneously, offering a ranking that considers experiences with all labels or a limited number of them. CM was integrated with sequential forward selection in a related context in [42]. Optimization approaches frame the task of ML feature selection as a constrained optimization problem, which can be addressed using various methods. These methods often aim to balance the performance and utility of the selected subset of features.