تحلیل تمایز میانگین محلی فازی بهبود یافته از طریق بهینه‌سازی تکراری برای تبدیل ویژگی و طبقه‌بندی
الموضوعات : مجله فناوری اطلاعات در طراحی مهندسیسعید معدنی 1 , محمد حسین معطر 2 , یحیی فرقانی 3
1 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
2 - دانشکده مهندسی کامپیوتر، دانشگاه آزاد اسلامی، مشهد، ایران
3 - دانشگاه آزاد اسلامی، واحد مشهد، مشهد، ایران
الکلمات المفتاحية: تحلیل تمایز میانگین محلی فازی (FLMDA), تحلیل تمایز خطی (LDA), کاهش ابعاد, استخراج ویژگی, طبقه‌بندی,
ملخص المقالة :
تحلیل تمایز میانگین محلی فازی (FLMDA) یک روش کاهش بعد نظارت شده است. روش FLMDA طلاعات محلی را برای ساختن پراکندگی‌های بین‌کلاسی و درون‌کلاسی جمع‌آوری می‌کند. با این حال، پس از تبدیل ویژگی با استفاده از FLMDA، نقاط داده نزدیک ممکن است متفاوت باشند. این واقعیت می‌تواند عملکرد طبقه‌بندی را کاهش دهد. در روش پیشنهادی، فرآیند استخراج ویژگی بر اساس لیست داده‌های مجاور پس از تبدیل تکرار می‌شود و این فرآیند تا همگرایی ادامه می‌یابد. بنابراین، فرض بر این است که اطلاعات محلی تا حد امکان حفظ شده و تمایز محلی بین نمونه‌های کلاس‌های مختلف افزایش می‌یابد. آزمایش‌های انجام‌شده بر روی مجموعه داده‌های مختلف دانشگاه کالیفرنیا، ایروین (UCI) برتری روش پیشنهادی را در مقایسه با روش‌های مشابه نشان می‌دهد.
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Information Technology in Engineering Design https://sanad.iau.ir/journal/ited | |
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Improved Fuzzy Local Mean Discriminant Analysis via Iterative Optimization for Feature Transformation and Classification
Saeed Maadani (1) Mohammad Hossein Moattar(2) * Yahya Forghani(3) (1) Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran (2) Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran* (3) Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
(Date received:1403/08/17 Date accepted:1403/11/16)
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Abstract Fuzzy Local Mean Discriminant Analysis (FLMDA) is a supervised dimensionality reduction method. FLMDA gathers local information for constructing between-class and within-class scatters. However, after feature transformation using FLMDA, the neighboring data points may differ. This fact may degrade the classification performance. In the proposed method, the feature extraction process is repeated based on the list of adjacent data after transformation, and this process continues until convergence. Therefore, it is supposed that the local information is preserved as much as possible and the local discrimination between the instances of different classes is increased. The experiments performed on different University of California, Irvine (UCI) datasets show the superiority of the proposed method compared to similar methods. Keywords: Fuzzy Local Mean Discriminant Analysis (FLMDA), Linear Discriminant Analysis (LDA), Dimensionality reduction, Feature extraction, Classification
Corresponding author:* Mohammad Hossein Moattar Address: Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Email: moattar@mshdiau.ac.ir | |
1- Introduction
In machine learning tasks, dimensionality reduction leads to the discovery of the hidden structure of the data [1]. Dimensionality reduction methods are divided into two groups of methods namely feature selection [2-17] and feature extraction. In features extraction (FE) methods, each extracted feature is a linear or nonlinear combination of initial features. Therefore, FE methods are divided into linear [18, 19] and nonlinear approaches [20-29]. Principle Component Analysis (PCA) [19] and Linear Discriminant Analysis (LDA) [18] are among the most widely used linear feature transformation and extraction methods. Amon these approaches, LDA is a supervised feature transformation method. The purpose of LDA is to find features that make the data more separable and performs it by maximizing the between-class scatters and minimizing the within-class scatters.
In LDA, the Euclidean measure or L2 norm is used to calculate the data distribution. Many papers have been presented based on the LDA method such as [30, 31]. Zhong et al. [32] introduced a noise resistant version of LDA called L1-LDA. In their model, instead of L2 norm, L1 norm is used for calculating scatterings. It is claimed that, L1 norm has lower sensitivity to noise. Zhang et al. [33] also claimed that their proposed method unlike L1-norm that is sensitive to outliers, via integrating global and local structure information is robust to outliers. Yang et al. [34] proposed 2D-LDA methodology in 2005 for 2-dimensional data. In their method, there is no need to vectorize image data. It is claimed that the time complexity of the method is lower than the LDA method, and can sometimes lead to the extraction of more discriminative features. Li et al. [35] proposed L1-2DLDA, which combines the advantages of two previously introduced approaches, namely L1-LDA and 2D-LDA.
In the LDA, new features are extracted so that the distance between each data of the same class is minimized and the distance between data from different classes is maximized. While, in particular, when classification methods such as k-nearest neighbor is supposed to be used to classify data, what is needed is to minimize the average distance of each data from the neighbors with the same class labels and maximize the average distance from the neighbors belonging other classes. This idea is developed in [36] and [37] which proposed the local LDA method. In [38] a Fuzzy-Gaussian two-directional inverse FDA is proposed which computes the fuzzy and Gaussian membership values to obtain the class-wise and global means. The Authors of [39] have proposed a new feature extraction method using entropy based fractional fuzzy in which uncertainty is incorporated into 2D-LDA. In this regard, a fuzzy logic-based feature extraction technique, called fuzzy generalized two-dimensional FLDA is proposed in [42]. Also, a new collaborative representation-based fuzzy discriminant analysis is proposed in [43].
Similarly, some articles such as [40, 41, 44-46] use fuzzy set theory. Jie Xu et al. [36], using the theory of "fuzzy sets", introduced a new feature extraction algorithm in 2016, which they called Fuzzy Local Mean Discriminant Analysis (FLMDA). In this method, the new features are extracted so that the distance of each data point from the weighted average of the adjacent data of the same-class is minimized and the distance between each data point and the weighted average of adjacent other-class data is maximized. In FLMDA, if a data point is on the boundary of the two classes, less weight is given to it due to less certainty for the class label.
After performing FLMDA, the data adjacencies may change, which may in turn causes that the previous adjacencies for which the scatterings are calculated are not valid anymore which may lead to performance degradation. Therefore, in the proposed method, the feature extraction process is iterated based on the FLMDA method and the new list of adjacent data, and this process continues until convergence. Using this approach, we expect that the classification accuracy is enhanced while clearly the complexity of feature mapping may increase. The rest of this paper is organized as follows. In Section 2, the FLMDA approach is described with more detail. The proposed method is explained in Section 3. In Section 4, the results of the experiments are discussed and Section 5 includes the conclusions and direction for future work.
2- Fuzzy Local Mean Discriminant Analysis (FLMDA)
A single label training data belongs to just one class. In other words, the degree of membership of a training data to a class is equal to one and the degree of its membership to other classes is zero. Suppose 
is the predefined degree of membership of the training data 
to the ith class. Equation (1) shows how to calculate the degree of membership of this training data to the class.
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The proposed algorithm | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Input: The training dataset d: final dimension, thr: threshold value Output: The final projection matrix, p Step 1: initialize p=I Step 2: feature extraction: for each i compute Step 3: Compute Step 4: Fixing Step 5: If the Frobenius norm of two consecutive values of P the higher than thr go to step 2 else the algorithm is terminated.
4- Experiments In this section, the proposed method and other methods such as LDA and FLMDA are evaluated using nine real data sets from UCI repository, namely Wine, Glass, Parkinson, Sonar, Yeast, Breast Tissue, Haberman's Survival, Iris and Ecoli. Each time using one of the three mentioned methods, the feature extraction takes place, and then, using the KNN method, the data is classified in the transformation space. 10-fold Cross Validation is used to evaluate the classification accuracy. Table 1 shows the highest accuracy obtained, the rank of each method among the three methods, the value of optimal parameters and the runtime of each method. The optimal value of the k at the stage of feature extraction and classification is selected from the set of {1, 5, 10, 15, 20, 25, 30}and the optimal value for the number of extracted features or the dimension of the data after the feature reduction, namely r, is chosen from the set of {1, 2, …, M}. We first select the optimal value of k from the set {1, 2, 3,…, 30} and then repeat the experiments with less values of k, from the set of {1, 5, 10, 15, 20, 25, 30} and we compared the results. We saw that the results of the experiments converged for both sets, and both sets of k had almost similar results. It can be inferred that the proposed method has a higher accuracy for the set of different values of k than the other two methods. Therefore, we considered the results for convenience in the set of {1, 5, 10, 15, 20, 25, 30}.
Table 1: performance comparison of different feature extraction methods
The results of Table 1 show that the proposed method, except in one case (i.e. average accuracy on glass data set), has higher accuracy compared to LDA and FLMDA methods. The lowest rate of accuracy using each of the three LDA, FLMDA and proposed methods was on glass dataset. This may denote that the proposed approach degrades on highly complex datasets and on datasets with medium complexity, the approach outperforms similar approaches. Also as denoted in Table 1, LDA has comparable results as the original FLMDA method and in 3 out of 9 experiments has outperformed FLMDA while for one dataset the results were actually the same. Also except for one dataset (i.e. Wine), the best dimensionality which has resulted the best KNN classification accuracy is almost the same for all three approaches, which is due to the same objective and metric used in these methods. According to Table 1, the execution time of the LDA algorithm is lower than that of FLMDA. To justify it, it should be said that although in both methods the To test the significance of the proposed approach compared with the two other methods, we applied the Wilcoxon ranked sum test with the following hypothesis: H0: “There is no significant difference between approaches”. H1: “There is significant difference between approaches”.
Table 2: Outputs of Wilcoxon ranked sum test for the evaluated approaches.
Having 9 experiments for each approach, the Wilcoxon critical value for two-tails test with α=0.05 level of significance, is 6. The output of the mentioned test is showed in Table 2. Bold numbers show the test for which the output of the Wilcoxon test is equal to or lower than the critical value and the H0 hypothesis cannot be rejected which means that there is a significant difference between the approaches. Figure 1 illustrates the accuracy of three feature extraction methods across varying values of k, paired with the designated classifier, while Figure 2 depicts their accuracy in relation to the final latent space dimension. Across both figures, the overall trends of the three methods remain consistent, displaying similar curve shapes for all datasets. This indicates that the sensitivity of these approaches to k (or the latent space dimension) is comparable, highlighting the importance of selecting an appropriate value for k to optimize performance. The uniformity in curve shapes suggests that all methods share a common dependency on this hyperparameter, making its careful tuning a critical aspect of achieving optimal results. Moreover, despite the similarities in trend, the proposed approach demonstrates a notable advantage in terms of area under the curve (AUC) when compared to the other two methods. This superiority is evident across most datasets, with the Glass dataset being a notable exception. The higher AUC values for the proposed approach suggest its robustness and effectiveness, particularly when the optimal k or latent space dimension is determined. These findings underline the importance of the proposed method as a more reliable option for feature extraction, further emphasizing its potential benefits in applications requiring high sensitivity and accuracy.
Fig. 1: The effect of the parameter k on the accuracy of the methods for various data sets The experiments illustrated in Figure 1 reveal that the value of k significantly influences the performance and accuracy of all three approaches. For datasets such as Wine, Iris, Glass, Breast Tissue, and Ecoli, lower values of k generally yield better results, suggesting that reduced dimensionality enhances performance for these datasets. Conversely, for datasets like Yeast and Parkinson, higher k values improve accuracy, indicating that retaining more dimensions is beneficial in these cases. This variation can be attributed to dataset characteristics, including the size of the dataset and the degree of class imbalance, which affect the suitability of specific k values. From these observations, it can be concluded that the optimal choice of k is dataset-dependent. When dealing with balanced datasets with a relatively small number of samples, lower k values are advantageous, as they simplify the feature space without sacrificing critical information. On the other hand, for larger datasets with significant class imbalance, higher k values may preserve more information from the latent space, leading to improved performance. These findings emphasize the importance of tailoring the k parameter to the specific characteristics of the dataset to achieve optimal results.
Fig. 2: The effect of changing the dimensionality on the accuracy of the methods for different data sets
Figure 2 highlights the impact of dimensionality on the performance of the three feature extraction approaches, with the experimented dimensions varying according to the size of the original feature space. The proposed method demonstrates superior performance compared to the other two approaches in most cases, with the exception of the Glass dataset, previously discussed, and the Iris dataset, where the LDA approach achieves a better area under the curve (AUC). Notably, the performance of the proposed method generally improves as the dimensionality of the mapping space increases, suggesting its robustness and adaptability to higher-dimensional feature transformations. However, two notable exceptions are observed with the Sonar and Parkinson datasets, where performance degrades as the dimensionality increases. This decline may stem from the smaller latent dimensionality in these datasets, which limits the effectiveness of mapping to higher dimensions. These findings indicate that while the proposed approach excels in most cases, the relationship between dimensionality and performance is not universal and depends on dataset-specific characteristics. It underscores the importance of carefully selecting the dimensionality of the transformation space to optimize results, particularly for datasets with unique feature distributions. On the other hand, as shown in Figure 2, the LDA approach has outperformed the other two methods in several instances, particularly for datasets like Iris. LDA's superior performance on the Iris dataset can be attributed to its simplicity and effectiveness in handling datasets with well-defined class separations. The linear nature of LDA makes it particularly suitable for such cases, where more complex methods such as FLMDA may not provide significant additional benefits. This suggests that, in scenarios with clear class boundaries and lower-dimensional feature spaces, simpler techniques like LDA can be more effective and computationally efficient than more intricate methods. Furthermore, success of the proposed approach in many cases highlights its robustness and efficiency in scenarios where the complexity of the data does not require the added sophistication of more advanced techniques. While more complex approaches might offer better results in some cases, the Iris dataset exemplifies situations where LDA's simplicity and low computational cost provide an optimal balance between performance and efficiency. This underscores the importance of choosing the right method based on the complexity of the dataset and the specific characteristics of the problem at hand.
5- Conclusion In the FLMDA method, the data is transmitted using a linear transformation to a new space with a lower dimensionality, so that the ration between local between-class scatters and the local within-class scatters in that space is maximized. Local within-class scatters are the total distance of each data from the weighted average of the neighboring same-class data, while local between-class scatters is the total distance of each data from the weighted average of the neighboring other-class data. After feature transformation using FLMDA method, the data adjacencies may change. Therefore, in the proposed method, the feature extraction process is iterated based on the FLMDA method and the lists of new adjacent data are generated, and this trend continued until convergence. Experiments are performed on 9 real UCI repository datasets showed that the feature extraction using the proposed method can increase the classification accuracy compared to the LDA and FLMDA. More precisely, the proposed method is more accurate compared to LDA and FLMDA, except in one case, i.e. the glass dataset. The results show that the average accuracy of the proposed method increased by 2.837% compared to LDA and 1.171% compared to FLMDA and by 0.68% compared to the maximum of the two methods. Meanwhile, the implementation time of the proposed method is approximately twice as long as the original FLMDA method. Even though the trials indicate modest gains in accuracy and computing complexity, it is still worthwhile to talk about how the suggested approach will affect real-world uses. Numerous fields could gain from the enhancements in feature transformation and classification accuracy, including: • Medical Diagnosis: By increasing the accuracy of disease classification in high-dimensional medical datasets such as gene expression profiles, EEG, and ECG, the technique may make it possible to develop more accurate diagnostic instruments for cardiac and neurological conditions. • Image Recognition: Especially for noisy or overlapping datasets, the technique may improve recognition rates in satellite imaging, object detection, and facial recognition. • Text and Document Classification: By enhancing text feature representation, the technique may improve natural language processing applications such as topic modelling, spam detection, and sentiment analysis. • Recommendation Systems: By better classifying user preferences and product categories, the method may improve recommendation systems. • Financial Data Analysis: By correcting class imbalances and overlapping data, the technique may improve predictive reliability in financial forecasts and fraud detection. Since a main issue of the proposed approach is the selection of k value, finding a metric for determining the best value of k is crucial as a direction for future works. Grid search can be used to systematically test a range of k values and identify the one with the best performance metric. Alternatively, data-driven methods like the Elbow Method analyze the trade-off between within-class scatter and between-class scatter as k varies. Heuristic approaches based on domain knowledge can also guide the selection of k. Finally, optimization-based methods, such as using genetic algorithms or Bayesian optimization, can automate and refine the k selection process. Also, due to considerable complexity of the proposed approach, more efficient implementation of the method is necessary for the applicability of the proposed method. Also, we can study on the applicability of the proposed speedup on the recently published approaches such as [38, 39, 42, and 43].
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