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Human activity recognition is one of the most important core building blocks behind many applications on smartphone such as medical applications, fitness tracking, context-aware mobile, human survey system, etc. This paper describes a robust system for human activity recognition by smartphone. Different from other work, we investigated the use and combination feature selection and instance selection...
Intrusion detection is used to protect the system from inside and outside attacks. Evolutionary algorithm has an important role in intrusion detection. Evolutionary algorithms are highly responsive for feature space reduction. The minimal number of features can improve the performance of an intrusion detection system. Thus we propose an intrusion detection system with various feature selection methods...
For classification of High Dimensional data, feature selection is the most important step for obtaining optimal result with respect to processing power required and time taken. Feature selection is a method by which the most relevant feature is selected from a set of features containing redundant and irrelevant features thereby reducing the load on the classification algorithm. This paper proposes...
Breast cancer is the most common malignant tumor for women. In the past twenty years, the incidence of breast cancer continues to rise. Then, the diagnosis and treatment of the breast cancer have become an extremely urgent work to do. In this study, we intend to build a diagnostic model of breast cancer by using data mining techniques. A feature selection method, INTERACT is applied to select relevant...
Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field...
In this paper, we propose a multi-stage feature selection algorithm, which focuses on the reduction of redundant features and the improvement of classification performance using feature ranking (FR), correlation analysis (CA) and chaotic binary particle swarm optimization (CBPSO). In the first stage, with the purpose of selecting the most effective features for classification, FR is introduced to...
Flooding based DoS attack represents one of most danger attacks in computer networks. Maximizing the effectiveness of flooding based DoS Attack detection accuracy is the main concerns of many researchers. So, many of them are focusing on increasing the detection effectiveness by features reducing. However, limited research studies have concentrated on investigation the correlation between features...
Today's real time applications data are stored in relational databases. In conventional approach to mine data, we often use to join several relations to form a single relation using foreign key links, which is known as flatten. Flatten may cause problems such as time consuming, data redundancy and statistical skew on data. Hence, how to mine data directly on numerous relations become a critical issue...
Ensemble systems are composed of a set of individual classifiers, organized in a parallel way, that receive the input patterns and send their output to a combination method, which is responsible for providing the final output of the system. The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among...
MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18–24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the...
In this paper, a ensemble learning classification algorithm based on the novel feature selection method is proposed. The feature selection method takes full account of the discrimination and class information of each feature by calculating the scores. Specially, the scores are fused for getting a weight for each feature. We select the significant features according to the weights. The result of feature...
Identifying review manipulation has become one of hot research issues in e-commerce because more and more customers make their purchase decisions based on some personal comments from virtual communities and e-business websites. Customers consider these personal reviews are more reliable than the existing internet advertisements. Consequently, some enterprises attempt to create fake personal comments...
This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset...
This paper describes research on the use of feature selection techniques to find correlation between single-nucleotide-polymorphism (SNP) in genes with the lupus disease in Genome-Wide Association (GWA) study. Feature selection is the process of selecting features that are correlated and discarding features that have no correlation in data mining. In this research, feature selection techniques will...
Web Application Firewalls (WAFs) analyze the HTTP traffic in order to protect Web applications from attacks. To be effective, WAFs need to analyze the payload of the packets. One of the techniques used for intrusion detection is to extract features from the payload by means of n-grams. An n-gram is a subsequence of n items from a given sequence. The number of n-grams is 256 to the nth power. Since...
In general, music retrieval and classification methods using music moods use a lot of acoustic features similar to music genre classification. These features are used as the spectral features, the rhythm features, the harmony features, and so on. However, all of these features may not be efficient for music retrieval and classification using music moods. Hence, in this paper, we propose a feature...
Protein features are often complex, and they are challenging to classify. In identifying the most discriminatory features in protein sequences, we propose a new feature-selection strategy by integrating the multivariate filter and Particle Swarm Optimisation (PSO) algorithms. Experimental results, based on the number of reducts and classification accuracy, were analysed in both the filter and wrapper...
Micro array data have a low instance-count and high dimensionality problem which prevent classifiers from building accurate models. This may result in significantly different classification accuracies across classifiers and features chosen. Therefore it is important to select the classifier and feature selection method that perform well on a specific data set. This paper proposes a novel criterion...
Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence,...
This paper analyzes the concentration and dispersion of the integrated feature selection algorithm (TFFS),and finds their shortcomings: it is difficult for concentration to measure the weigh of the low frequent terms; dispersion ignores the impact of term whose mutual information is negative. Propose a modified feature selection algorithm (TFFSL), which makes certain improvements on concentration...
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