Feature Subset Selection

Generally Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant features as well as possible. This is because firstly irrelevant features that do not contribute predictive accuracy, and secondly redundant features that do not redound to getting a better predictor for that they provide mostly information which is already present in other feature. Traditionally, the feature subset selection research has focused on searching for relevant features. A Relief method is example of this, which weighs each feature according to its ability to discriminate instances under different targets based on distance-based criteria function. However, Relief is ineffective at removing redundant features as two predictive but highly correlated features are likely both to be highly weighted. CFS and FCBF [20], are examples that take into consideration the redundant features. CFS is achieved by the hypothesis that a good feature subset is one that contains features highly correlated with the target, yet uncorrelated with each other. FCBF [20] is a fast filter method which can identify relevant features as well as redundancy among relevant features without pair wise correlation analysis. The FAST algorithm [19] employs the clustering-based method to choose features. Recently, hierarchical clustering has been adopted in word selection in the context of text classification. As distributional clustering of words are agglomerative in nature, and result in suboptimal word clusters and high computational cost. Dhillon et al. [23] proposed a new information-theoretic divisive algorithm for word clustering and applied it to text classification. Butterworth et al. [24] proposed to cluster features using a special metric of Barthelemy

The k-value of 7 produces the best overall accuracy. The feature subset and feature weighting tasks both dis- play slight improvements or retention of the performance for all values of k. The Wisconsin dataset has the largest number of features (9) of the datasets discussed here and it is to be expected that datasets with larger numbers of features will have improved performance when applying techniques to adjust the importance and impact of the features. However, it is worth noting that the feature subset selection and feature weighting techniques used in this prototype assume that the features operate inde- pendently from each other. This may not be the case, especially when applying these techniques to classifica- tion using low-level analysis of media objects.

number of features in many applications where data has multiple features. FS is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing the irrelevant (and the redundant) features. It has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. FS techniques aim at reducing the number of unnecessary features in classification rules. The features are measured by their necessity in heuristic FS techniques. The proposed framework uses filter method to remove irrelevant features, clustering-based method to remove redundant features and Rough Set Theory (RST) with greedy heuristics for feature subset selection.

Feature subset selection is the process of choosing a subset of good features with respect to the target concept. A clustering based feature subset selection algorithm has been applied over software defect prediction data sets. Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed. A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software projects. Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development. The proposed clustering based algorithm for feature selection uses minimum spanning tree based method to cluster features. And then the algorithm is applied over four different data sets and its impact is analyzed.

In email spam detection, not only different parts and content of emails are important, but also the structural and special features of these emails have effective rule in dimensionality reduction and classification accuracy. Because spammers constantly change patterns of spamming messages using different advertising images and words to form new pattern features or attributes, feature subset selection and ensemble classification are necessary to address these issues. Recently, various techniques based on different algorithms have been developed. However, the classification accuracy and computational cost are often not satisfied. This study proposes a new ensemble feature selection techniques for spam detection, based on three feature selection algorithms: Novel Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (BQPSO) Algorithm, and Binary Quantum Gravitational Search Algorithm (BQGSA) along with the Multi-layer Perceptron (MLP) classifier. The achieved results showed accuracy very near to 100% in email spam detection. Keywords: binary bat algorithm, binary quantum particle swarm optimization, binary quantum gravitational search algorithm, multi- layer perceptron.

The aim of choosing a subset of good features with respect to the target concepts, feature subset selection is an effective way for reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Feature subset selection is an effective way for reducing dimensionality, eliminating irrelevant data and redundant data, increasing accuracy. There are various feature subset selection methods in machine learning applications and they are classified into four categories: Embedded, wrapper, filter and hybrid approaches. Based on the MST method, we propose a Fast clustering-bAsed feature Selection algoriThm (FAST).The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph- theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form the final subset of features.

As can be seen in Figure 2, it is evident that the proposed solution takes high dimensional dataset as input and performs feature subset selection which gets subset of features that are representatives of all clusters. This will improve the performance of selection process. Once features are selected, further processing can be possible that is based on the application requirements.

the classification therefore the system should be able to find the description of each class [2]. Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm is basically evaluated from the efficiency and effectiveness points of view. The time required to find a subset of features is concerned with the efficiency while the effectiveness is related to the quality of the subset of features. Some feature subset selection algorithms can effectively eliminate irrelevant features but fail to handle redundant features yet some of others can remove the irrelevant while taking care of the redundant features. A Fast clustering-based feature selection algorithm (FAST) is proposed which is based on above criterion handling redundancy and irrelevancy. [1] The Minimum Spanning tree (Kruskal’s algorithm) is constructed from the F-Correlation value which is used to find correlation between any pair of features. Kruskal's algorithm is a greedy algorithm in graph theory that finds a minimum spanning tree for a connected weighted graph. It finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized.

In the application of Conditional Random Fields (CRF), a huge number of features is typically taken into ac- count. These models can deal with inter-dependent and correlated data with an enormous complexity. The application of feature subset selection is important to improve performance, speed and explainability. We present and compare filtering methods using infor- mation gain or χ 2 as well as an iterative approach for

The research, a new approach for the feature selection before clustering mechanism has been presented. Feature Subset Selection method used to choose a subset of the original features to be used for the subsequent processes. In this algorithm, initially collect the preprocessed dataset then find out the ranges of the respected dataset. After that, select the key features in the preprocessed dataset based on the threshold values. Interaction K-means (IKM), a partitioning clustering algorithm used to detect clusters of objects with similar interaction patterns classification and clustering. Interaction K-means (IKM), a partitioning clustering algorithm suitable to detect clusters of objects with similar interaction patterns classification and clustering. Finally, Best Ranking algorithm used to select the best cluster for assuring best result.

For testing our methods we used two public datasets, one representing colon (tumor/normal) tissues [10], and the other representing acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) [9] (see also Materials and methods). For both these datasets we perform cross-valida- tion tests on the two-class problem, distinguishing between ALL and AML in the ALL/AML dataset and normal and tumor in the colon dataset. By dividing the data into a train- ing set and a test set several times, we compare the average performance of different prediction methods on the test set using four different feature subset selection (FSS) proce- dures on the training set. In this study we use two linear dis- criminant methods: diagonal linear discriminant (DLD) and Fisher’s linear discriminant (FLD), and one local method; k nearest neighbors (kNN) (for kNN and FLD, see [12]). The FSS procedures we use are all pairs, greedy pairs, forward selection and individual ranking. The application of several prediction methods is to see if the differences between the FSS procedures are specific to a particular prediction method rather than more general. Instead of comparing the different prediction methods we compare the ability of the different FSS procedures to find feature subsets that gener- alize the class differences. A comparison is done for feature subsets of size 2, 4, 6,…, m, where m is the number of experi- ments in the dataset. We also leave out different portions Figure 1

In this paper we consider different contextual and orthographic word-level features. These features are language independent in nature, and can be very easily derived for almost all the languages with a very little effort. Thereafter GA is used to search for the appropriate feature selection. Here, features are encoded in the chromosomes with binary encoding scheme. Adap- tive mutation and crossover operators are used to accelerate the convergence of GA. We also use elitism. In order to compute the fitness of each chromosome, ME classifier is evaluated with the features encoded in the particular chromosome and the average F-measure value is calculated for the 3-fold cross validation on training data.

filter method in this paper. When any kind of business data was first stored on computers that improves a continuous data access that allows users to get the data in real time. In the case of filter feature selection methods, the clustering application has to demonstrate to be more reliable than conventional feature selection algorithms. For reducing the dimensionality of text data the distributed clustering of words can be used. Our proposed FAST algorithm uses minimum spanning tree based method to cluster features which can be derived from other clustering based algorithms.

Abstract — Feature selection is the mode of recognize the good number of features that fabricate well-suited outcome as the unique entire set of features. Feature Extraction is the special form of dimensionality reduction where feature selection is the subfield of feature extraction. Feature selection algorithms essentially have two basic criteria named as, time requirement and quality. The core idea of feature selection process is improve accuracy level of classifier, reduce dimensionality; speedup the clustering task etc., this paper mainly focuses on Comparison of various techniques and algorithms for feature selection process.

In Big Data, Massive data faces a problem of classification. When there is high dimensionality of datasets, clustering becomes very slow. Feature selection is the part of the clustering process. Feature selection techniques are defined as a subset of the feature extraction field. It selects features that are relevant to the target concepts. The advantage of feature selection include reducing the data size when superfluous features are discarded, improving the Classification/prediction accuracy. When using feature selection, data that contain redundant as well as relevant features are removed.

A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation measure which scores the different feature subsets. The simplest algorithm is to test each possible subset of features finding the one which minimizes the error rate. This is an exhaustive search of the space, and is computationally intractable for all but the smallest of feature sets. The choice of evaluation metric heavily influences the algorithm, and it is these evaluation metrics which distinguish between the three main categories of feature selection algorithms: wrappers, filters and embedded methods.

A feature selection algorithmic can be seen as the combination of a search technique for proposing new feature subsets, at the side of associate analysis live that scores the various feature subsets. The only algorithmic program is to check every possible set of features finding the one that minimizes the error rate. This can be associate exhaustive search of the space, and is computationally intractable for all but the smallest of feature sets. The selection of evaluation metric heavily influences the algorithmic program, and it's these analysis metrics that distinguish between the 3 main categories of feature selection algorithms: wrappers, filters and embedded methods.

I N the literature many approaches have been proposed for dimensionality reduction [1], [2], [3]. The existing dimensionality reduction methods can roughly be categorized into two classes: feature extraction and feature selection. In feature extraction problems [3], [4], the original features in the measurement space are initially transformed into a new dimension-reduced space via some specified transformation. Significant features are then determined in the new space. Although the significant variables determined in the new space are related to the original variables, the physical interpretation in terms of the original variables may be lost. In addition, although the dimensionality may be greatly reduced using some feature extraction methods, such as principal compo- nent analysis (PCA) [5], the transformed variables usually involve all the original variables. Often, the original variables may be redundant when forming the transformed variables. In many cases, it is desirable to reduce not only the dimensionality in the transformed space, but also the number of variables that need to be considered or measured [6], [7].

Abstract— Feature selection is an important pre-processing step for pattern recognition. It can discard irrelevant and redundant information that may not only affect a classifier’s performance, but also tell against system’s efficiency. Mean- while, feature selection can help to identify the factors that most influence the recognition accuracy. The result can provide valuable clues to understand and reason what is the underlying distinctness among human gait-patterns. In this paper, we introduce a computationally-efficient solution to the problem of human gait feature selection. We show that feature selection based on mutual information can provide a realistic solution for high-dimensional human gait data. To assess the performance of the proposed approach, experiments are carried out based on a 73-dimensional model-based gait features set and a 64 by 64 pixels model-free gait symmetry map. The experimental results confirmed the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant accuracy loss, which outperforms correlation and ANOVA based methods.

Our experiments have been implemented in five steps that are training dataset pre-processing, subset generation, model validation, model evaluation and model comparison. This experiment has been implemented by using best first search technique in a wrapper model to select the significant features by using seven classifier algorithms. These classifier algorithms produced best 16 features set. The proposed system has been presented novel AdaBoost ensemble learning algorithm which is based on BN learning using GA search method. The novel AdaBoost ensemble learner algorithm improved the performance metrics of BN learning using GA search method which introduced high accuracy rate (AR), precision rate, recall, KS, and confusion matrix and MAE. The novel AdaBoost ensemble learner algorithm reduced the classification time, which can be used for an adaptive technique where the other AdaBoost technique consumes much more time for learning dataset. The reduction of classification time increased the training speed so it may be used in the multi-core architecture. When we used GA for learning the BN, it will prevent the overfitting of training data. The structure of novel AdaBoost ensemble learner algorithm represented the correlation among the attributes of the dataset.