Two-stage Feature Selection Method Research Articles

A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm

Text categorization is widely used when organizing documents in a digital form. Due to the increasing number of documents in digital form, automated text categorization has become more promising in the last ten years. A major problem of text categorization is its large number of features. Most of those are irrelevant noise that can mislead the classifier. Therefore, feature selection is often used in text categorization to reduce the dimensionality of the feature space and to improve performance. In this study, two-stage feature selection and feature extraction is used to improve the performance of text categorization. In the first stage, each term within the document is ranked depending on their importance for classification using the information gain (IG) method. In the second stage, genetic algorithm (GA) and principal component analysis (PCA) feature selection and feature extraction methods are applied separately to the terms which are ranked in decreasing order of importance, and a dimension reduction is carried out. Thereby, during text categorization, terms of less importance are ignored, and feature selection and extraction methods are applied to the terms of highest importance; thus, the computational time and complexity of categorization is reduced. To evaluate the effectiveness of dimension reduction methods on our purposed model, experiments are conducted using the k-nearest neighbour (KNN) and C4.5 decision tree algorithm on Reuters-21,578 and Classic3 datasets collection for text categorization. The experimental results show that the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure.

Multimodal microscopy for automated histologic analysis of prostate cancer

BackgroundProstate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples.MethodsWe recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating forward selection (SFFS), were applied to classify tissue samples as cancer or non-cancer.ResultsWe achieved high accuracy (area under ROC curve (AUC) >0.97) in cross-validations on each of two data sets that were stained under different conditions. When the classifier was trained on one data set and tested on the other data set, an AUC value of ~0.95 was observed. In the absence of IR data, the performance of the same classification system dropped for both data sets and between data sets.ConclusionsWe were able to achieve very effective fusion of the information from two different images that provide very different types of data with different characteristics. The method is entirely transparent to a user and does not involve any adjustment or decision-making based on spectral data. By combining the IR and optical data, we achieved high accurate classification.

A Two-Stage Feature Selection Method for Gene Expression Data

Microarray data referencing gene expression profiles provide valuable answers to a variety of problems, and contributes to advances in clinical medicine. Gene expression data typically has a high dimension and a small sample size. Generally, only relatively small numbers of gene expression data are strongly correlated with a certain phenotype. To analyze gene expression profiles correctly, feature (gene) selection is crucial for classification. Feature (gene) selection has certain advantages, such as effective extraction of genes that influence classification accuracy, elimination of irrelevant genes, and improvement of the classification accuracy calculation. In this paper, we propose a two-stage feature selection method, which uses information gain to implement a gene-ranking process, and combines an improved particle swarm optimization with the K-nearest neighbor method and support vector machine classifiers to calculate the classification accuracy. The experimental results show that the proposed method can effectively select relevant gene subsets, and achieves higher classification accuracy than previous studies.

Diagnosis of tapping process using spindle motor current

A two-stage pair-wise feature selection and classification method is proposed to diagnose the tapping process using the electrical current signal of the spindle motor. The motor current is measured with a non-intrusive Hall effect sensor. The diagnostic process is divided into two stages. In the first stage, wavelet coefficients of the current signals are computed and a subset of these coefficients is chosen as the initial diagnostic features based on their discriminatory powers for each pair of the fault classes. At the second stage, principal component analysis (PCA) is applied to reduce the dimensionality of the initial diagnostic features. Non-linear radial basis probabilistic neural networks (RPNNs) are employed for pair-wise feature classification. The final diagnostic decision is made based on a statistical voting procedure. The proposed method has been demonstrated with experimental data. On average a 93% success rate has been achieved.