Feature clustering for pso-based feature construction on high-dimensional data

Feature construction (FC) refers to a process that uses the original features to construct new features with better discrimination ability. Particle Swarm Optimisation (PSO) is an effective search technique that has been successfully utilised in FC. However, the application of PSO for feature construction using high dimensional data has been a challenge due to its large search space and high computational cost. Moreover, unnecessary features that were irrelevant, redundant and contained noise were constructed when PSO was applied to the whole feature. Therefore, the main purpose of this paper is to select the most informative features and construct new features from the selected features for a better classification performance. The feature clustering methods were used to aggregate similar features into clusters, whereby the dimensionality of the data was lowered by choosing representative features from every cluster to form the final feature subset. The clustering of each features are proven to be accurate in feature selection (FS), however, only one study investigated its application in FC for classification. The study identified some limitations, such as the implementation of only two binary classes and the decreasing accuracy of the data. This paper proposes a cluster based PSO feature construction approach called ClusPSOFC. The Redundancy-Based Feature Clustering (RFC) algorithm was applied to choose the most informative features from the original data, while PSO was used to construct new features from those selected by RFC. Experimental results were obtained by using six UCI data sets and six high-dimensional data to demonstrate the efficiency of the proposed method when compared to the original full features, other PSO based FC methods, and standard genetic programming based feature construction (GPFC). Hence, the ClusPSOFC method is effective for feature construction in the classification of high dimensional data

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence