With this submission, a set of ensemble learning based methods for the MIREX 2015 Speech / Music Classification and Detection task is proposed and evaluated. The main algorithm for the Detection task employs a self similarity matrix analysis technique to detect homogeneous segments of audio that can be subsequently classified as music or speech by a Random Forest classifier. In addition to the ...

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...

Coronary Artery Disease (CAD) is one of the most prevalent diseases, which can lead to disability and sometimes even death. Diagnostic procedures of CAD are typically invasive, although they do not satisfy the required accuracy. Hence machine learning methods can be used, so that diagnosis can be made faster and with improved accuracy. There are many features that need to be taken into consider...

Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost and Random Forest ensembles across a collection of benchmark data sets. This paper carries out a lesion study on Rotation Forest in order to find out which of the parameters and the randomization heuristics are respon...

In fact, there exist three genres of intelligence architectures: logics (e.g. Random Forest, A∗ Searching), neurons (e.g. CNN, LSTM) and probabilities (e.g. Naive Bayes, HMM), all of which are incompatible to each other. However, to construct powerful intelligence systems with various methods, we propose the intelligence graph (short as iGraph), which is composed by both of neural and probabili...

I. Abstract This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models were constructed, with SVM giving the best results: an improvement of 12.9% over bin...

Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be re...

We present an extensive study on the performance of data agglomeration and decision-level fusion for robust cross-corpus emotion recognition. We compare joint training with multiple databases and late fusion of classifiers trained on single databases, employing six frequently used corpora of natural or elicited emotion, namely ABC, AVIC, DES, eNTERFACE, SAL, VAM, and three classifiers i. e. SVM...

We propose extensions of the classical JSM-method and the Naı̈ve Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classificati...

Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in ...