Random forest (RF) analysis of genetic data does not require specification of the mode of inheritance, and provides measures of variable importance that incorporate interaction effects. In this paper we describe RF-based approaches for assessment of gene and haplotype importance, and apply these approaches to a subset of the North American Rheumatoid Arthritis Consortium case-control data provi...

Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (AN...

Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) p...

Many remote sensing studies do not distinguish between natural and planted forests. We combine C-Band Synthetic Aperture Radar (Sentinel-1, S-1) optical satellite imagery (Sentinel-2, S-2) examine Random Forest (RF) classification of acacia plantations forest in North-Central Vietnam. demonstrate an ability to plantation from forest, with overall accuracies 87% for S-1, 92.5% 92.3% S-2 S-1 comb...

In this paper we present our work on the parametrization of Random Forests (RF), and more particularly on the number K of features randomly selected at each node during the tree induction process. It has been shown that this hyperparameter can play a significant role on performance. However, the choice of the value of K is usually made either by a greedy search that tests every possible value t...

We present a novel method to recognise semantic equivalents of biomedical terms in language pairs. We hypothesise that biomedical term are formed by semantically similar textual units across languages. Based on this hypothesis, we employ a Random Forest (RF) classifier that is able to automatically mine higher order associations between textual units of the source and target language when train...

Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in...

Diagnosis of health conditions is a very challenging task in field of medical science. In medical science, day by day data is increasing continuously and creates problem to identify the accurate diseases. Data mining based classification plays very important role in classification of data. In this research work we have used various data mining based classification technique to develop the class...

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure....

We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constraint set to prove that the corresponding LP relaxation solves the original int...