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Feature selection is one of the core issues in designing pattern recognition and machine learning systems, and has attracted considerable attention in the literature. In this paper, a new feature subset selection algorithm with conditional mutual information is proposed, which firstly guarantees to find a subset of which the mutual information with the(More)
Recent studies have indicated that long non-coding RNAs (lncRNAs) could act as non-invasive tumor markers in both diagnosis and predicting the prognosis. In this study, we focused to determine the expression of circulating lncRNAs in patients suffering from non-small-cell lung cancer (NSCLC), aiming to found the potential lncRNA as predictor. Twenty-one(More)
Costumer feature selection is one of the core issues of Costumer churn prediction in telecom industry. This paper proposes a hybrid two-phase feature selection method which can effectively reduce feature dimension and promote predicting performance by using both traditional expertise approach and Markov blanket discovery technique. Empirical results of a(More)
—Finding an efficient way to discover Markov blanket is one of the core issues in data mining. This paper first discusses the problems existed in IAMB algorithm which is a typical algorithm for discovering the Markov blanket of a target variable from the training data, and then proposes an improved algorithm λ-IAMB based on the improving approach which(More)
Write-optimized data structures like Log-Structured Merge-tree (LSM-tree) and its variants are widely used in key-value storage systems like Big Table and Cassandra. Due to deferral and batching, the LSM-tree based storage systems need background compactions to merge key-value entries and keep them sorted for future queries and scans. Background compactions(More)
In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handled on each class label. Super-efficiency(More)
Artificial neural networks (ANN) are an information-processing method of a simulation of the structure for biological neurons. This paper makes a research on the approach of the artificial neural network for fault diagnosis of short-wave radios and constructs a fault diagnosis system of short-wave radios with ANN. And the system can analyze fault phenomena(More)