We propose a pairwise local observation-based Naive Bayes (NBPLO) classifier for image classification. First, we find the salient regions (SRs) and the Keypoints (KPs) as the local observations. Second, we describe the discriminative pairwise local observations using Bag-of-features (BoF) histogram. Third, we train the object class models by using random forest to develop the NBPLO classifier f...

We address the problem of annotating a video sequence with partial supervision. Given the pixel-wise annotations in the first frame, we aim to propagate these labels ideally throughout the whole video. While some labels can be propagated using optical flow, disocclusion and unreliable flow in some areas require additional cues. To this end, we propose to train localized classifiers on the annot...

When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a Fuzzy Random Forest, is proposed. This ...

Cardiac Arrhythmia refers to a medical condition in which heart beats irregularly. This paper aims to detect and classify arrhythmia into 14 different variants. A few popular techniques from contemporary literature were implemented namely Naive Bayes, feature selection, SVM, Random Forests and Neural Networks.A new approach combining SVM and Random Forests classifiers was also implemented.

Quotations from financial leaders can have significant influence upon the immediate prospects of economic actors. Indiscreet or candid comments from senior business leaders have had detrimental effects upon their organizations. Established polarity classification techniques perform poorly when classifying quotations because they display a number of complex linguistic features and lack of traini...

The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers belonging to a specific target audience. This task has immense importance in the field of lexical simplification. Lexical simplification helps in improving the readability of texts consisting of challenging words. As a participant of the SemEval-2016: Task 11 s...

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 sma...

The paper examines the potential of a novel data mining method, the random forest classifier, to support managerial decision making in complex forecasting applications. A modelling paradigm is proposed that embraces a learning curve analysis and grid-search to analyse the model’s sensitivity towards the number of training examples and parameter settings, respectively, and, eventually, produce a...

Optical coherence tomography (OCT) has become one of the most common tools for diagnosis of retinal abnormalities. Both retinal morphology and layer thickness can provide important information to aid in the differential diagnosis of these abnormalities. Automatic segmentation methods are essential to providing these thickness measurements since the manual delineation of each layer is cumbersome...

Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often difficult to understand and explain resulting in barriers to their adoption and validation. Our work (RFEX) focuses on enhancing Random Forest (RF) ...