General Approaches For Feature Selection

In this module, the class label for the testing data is predicted. The n – dimensional feature vector for the testing data is converted from query tree of testing data in the manner similar to the data pre – processing phase. The SQLIA classifier determines the new testing feature vector is normal or malicious, by using optimized SVM classification model.

The major issue filter based feature subset methods are producing feature subset with maximum relevancy and minimum redundancy. The subset of features ‗S‘ are identified based on selecting feature subset at the multiple level . Finally, a subset of 19 % features are produced from the gives the better classifier performance when compared with other feature subsets methods . Extensive experiments are conducted on image data sets and shows that our framework for selecting optimal feature subset produced better efficiency and accuracy. The future scope of the work is to annotate the image regions and can retrieve relevant images based on image

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

The choices for the selection criterion have several model fit statistics that are useful for model

In this paper, a learning method, Support vector Machine, is applied on the different datasets for getting more enhanced results. SVM is introduced in the early 90’s, and they led to an explosion of interest in machine learning. SVM have been developed by Vapnik and are gaining popularity in the field of machine learning due to many advance functioning and efficient performance.

Naïve Bayes classifier is a simple but effective the Bayesian classifier built upon the strong assumption that different features are independent with each other. Classification is done by selecting the highest posterior of classification variable given a set of feature. Naive Bayes classifiers assume that the effect of a variable value on a given class is independent of the values of other variable. This assumption is called class conditional independence. An advantage of the naive Bayes classifier is that it requires a small amount of training data to estimate the variable values necessary for classification.

The machine learning block uses the random forest algorithm to analyze the data that is being fetched by the learning agent after which it analysis report is sent back where in it is used to create the diagnosis function which is the required output

Compared with other data mining application fields, medical data mining plays an important role and it has some unique characteristics. The medical data processing has the high potential in medical domain for extracting the hidden patterns within the dataset [15]. These patterns are used for clinical diagnosis and prognosis. The medical data are generally distributed, heterogeneous and voluminous in nature. The data ought to be integrated and collected to provide a user oriented approach to hidden patterns of data. An important problem in medical analysis is to achieve the correct diagnosis of certain important information. This paper describes classification algorithms and it is used to analyze the performance of these algorithms. The performance factor is used for analysis are accuracy and error measures. The accuracy measures are True Positive (TP) rate, F Measure, Receiver Operating Characteristics (ROC) area and Kappa Statistics. The error measures are Mean Absolute Error (M.A.E), Root Mean Squared Error (R.M.S.E), Relative Absolute Error (R.A.E) and Relative Root Squared Error (R.R.S.E) [5]. Section 2 describes the classification algorithms. Experimental results are analyzed in section 3 and section 4 describes the conclusion of this paper.

To handle the problem of high dimensionality of such data sets, a number of algorithms have been introduced, which use row-based enumeration techniques [6, 7] instead of using column-based enumeration algorithms such as [8-10]. These methods work based on the assumption that the data sets have thousands of columns or have a lot of dimensions, but smaller number of rows. Carpenter algorithm [6] uses a bottom-up method while the TD-close Algorithm [7] uses a top-down exploration approach. Both of these techniques however, work best for dense datasets. Also, these algorithms work only for high-dimensional datasets that have significantly lesser number of rows compared to number of columns. Different dimension reduction algorithms, such as Principal Component Analysis [11], Multi-Dimensional scaling [12] and Independent component analysis [13] are effective and popular algorithms for reducing dimensionality. However, the effectiveness of these different algorithms is limited due to their global linearity.

Association Rule- Association is the most popular data mining techniques and fined most frequent item set. Association strives to discover patterns in data which are based upon relationships between items in the same transaction. Because of its nature, association is sometimes referred to as “relation technique”. This method of data mining is utilized within the market based analysis in order to identify a set, or sets of products that consumers often purchase at the same time

- With the increment in IT (Information Technology) the size of the databases generated by the organizations due to the availability of low-cost store and the development in the data pick technologies is also increasing , Data mining (DM) also called KDD (Knowledge discovery in databases) helps to identifying priceless information in such large databases. This valuable information can help the decision producer to make exact futurity decisions, In Figure 1 describe data mining techniques

Abstract— Support Vector Machine (SVM) is regarded as a good alternative of the traditional learning classification. Terrain classification using polarimetric synthetic aperture radar (POLSAR) imagery has been a very active research field over recent years. Because of support vector’s excellent learning performance, it has become a research hot spot in the field of machine learning. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery and then we will describe two SVM methods: Normal represents the one without feature selection and weighted represents with optimal multi-feature selection. Obviously, the weighted SVM returns a sparse set of features and employs less SV’s than normal. However, the performance of weighted SVM is no worse and even better than the performance of normal one. The excellent recognition rates achieved in experiments indicate that advanced (weighted) SVM is well suited for SAR image classification. The good performance of the proposed methods is illustrated in three challenging remote sensing

On other hand of the proposed recognition system, another feature extraction is modified and optimized to be used known as

Feature plays a very important role in the area of image processing. Different feature extraction techniques are applied on different types of images to get features that will be useful in classifying and recognition of images. Features describes the important information of images that helps to classify images correctly and remarkably reduce the dimension of the images.

Pattern recognition is a technique to differentiate different pattern into classes through the help of supervised or unsupervised technique. We have developed highly sophisticated skills for sensing the environment and taking actions according to what we observe. This sensing and understanding is mostly dependent on ability to differentiate between patterns. The pattern recognition ability if with the help of machine learning can be applied in machine. The machine ability to make decision like human being will be enhanced. Many applications such as data mining, web searching, face recognition etc has already been in uses which are based on the pattern recognitions. The objective of this review paper is to summarize and compare some of the well-known methods and application used in pattern recognition system.