Enhanced Features extraction method based on Fuzzy C-meansalgorithm

Feature extraction is an essential process in machine learning (ML). It is the process of deriving new features from the original features in order to enhance the quality or representation of the data for different reasons, such as increasing classifier efficiency and allowing higher classification accuracy. Clustering is one of the methods used in feature extraction. There are different clustering methods: soft clustering and hard clustering. Hard clustering, where each data point must be assigned to one cluster. while soft clustering, that may be a data point assigned to more than one cluster with a specific parameter "membership" value. Therefore, the type of clustering method may affect on quality of features generated using clustering. As related to using soft clustering algorithms such as Fuzzy c-means (FCM), several issues can be examined about the impact of using it on feature extraction, i. e. enhancing the quality of these features or not. Also, the effect of data circumstances that are introduced to FCM algorithm on the results. This is the problem that was adopted in this thesis. The aim is to develop a new method for feature extraction using FCM clustering to enhance classification performance, which allows for a more accurate representation of the nature of the data by using the "partial membership" features. The proposed work consists of several steps. First, an algorithm based on FCM to produce the new features would be proposed, this is because allowing points to partially belong to more than one cluster can generate useful and rich representations of features. Then, for testing the quality of these features, a classification algorithm such as K-Nearest Neighbors (KNN) algorithm would be used. At last, for comparing the results, K-means algorithm, which belongs to the hard clustering method, was used in parallel with the proposed algorithm toIX extract features independently and evaluate these features through KNN algorithm. The practical work was applied to six datasets: CICIDS2017, Breast Cancer, Sonar, Spam base, Nomao, and Diabetes. After implementing the algorithms and getting the results, we notice the superiority of the proposed method in all types of data, with some variation between data, in terms of classification performance metrics: accuracy, precision, recall, and F1 score, demonstrating its effectiveness in generating high-quality and robust features for ML tasks. CICIDS2017 dataset has achieved an accuracy of 99.10%, Breast Cancer 96.71%, Sonar 89.66%, Spam base 88.23%, Nomao 95.03%, and Diabetes 65.51%. FCM algorithm is considered better than K-Means for reasons related to the nature of the data and the characteristics of each algorithm. It allows a single point to belong to more than one cluster with different degrees and produces more expressive features. It is also suitable for cases where the class boundaries are unclear or overlap with each other.