Combining Best Features Selection Using Three Classifiers in Intrusion Detection System
Nowadays, with the development of internet technologies service in the world, the intruders has been increased rapidly. Therefore, the advent of Intrusion Detection System (IDS) in the security of networks field prevents intruders from having access to the information. IDS plays an important role of detecting different types of attacks. Because network traffic dataset has many features, the process of feature selection and removing irrelevant features increase the performance of the classification algorithms accuracy. This paper provides three various methods which are: Firstly, Information Gain. Secondly, Gain Ratio. Thirdly, Correlation Feature Selection. These techniques are used for selecting and ranking features then select combine the best top ranking features. Only six features were selected out of 41 features. These features are tested on three classifiers (K-Nearest Neighbor, Naïve Bays and Neural Network based Multilayer Perceptron) for classification and detect intrusion. The outcome illustrates that a high level of attacks classification accuracy can be accomplished by combing best different features selection. Moreover, K-Nearest Neighbor gets high accuracy classification for IDS. The proposed model has been applied on KDD data set and ten cross validation process used to assess the classification performance.
Facial Expression Classification Based on SVM, KNN and MLP Classifiers
Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.
A Comparison of Three Classification Algorithms for Handwritten Digit Recognition
Handwritten digits recognition is considered as a core to a diversity of emerging application. It is used widely by computer vision and machine learning researchers for performing practical applications such as computerized bank check numbers reading. However, executing a computerized system to carry out certain types of duties is not easy and it is a challenging matter. Recognizing the numeral handwriting of a person from another is a hard task because each individual has a unique handwriting way. The selection of the classifiers and the number of features play a vast role in achieving best possible accuracy of classification. This paper presents a comparison of three classification algorithms namely Naive Bayes (NB), Multilayer Perceptron (MLP) and K_Star algorithm based on correlation features selection (CFS) using NIST handwritten dataset. The objective of this comparison is to find out the best classifier among the three ones that can give an acceptable accuracy rate using a minimum number of selected features. The accuracy measurement parameters are used to assess the performance of each classifier individually, which are precision, recall and F-measure. The results show that K_Star algorithm gives better recognition rate than NB and MLPas it reached the accuracy of 82.36%.