13 research outputs found
Comparing the Performance of Random Forest, SVM and Their Variants for ECG Quality Assessment Combined with Nonlinear Features
For evaluating performance of nonlinear features and iterative and non-iterative classification algorithms (i.e. kernel support vector machine (KSVM), random forest (RaF), least squares SVM (LS-SVM) and multi-surface proximal SVM based oblique RaF (ORaF) for ECG quality assessment we compared the four algorithms on 7 feature schemes yielded from 27 linear and nonlinear features including four features derived from a new encoding Lempel–Ziv complexity (ELZC) and the other 26 features. Seven feature schemes include the first scheme consisting of 7 waveform features, the second consisting of 15 waveform and frequency features, the third consisting of 19 waveform, frequency and approximate entropy (ApEn) features, the fourth consisting of 19 waveform, frequency and permutation entropy (PE) features, the fifth consisting of 19 waveform, frequency and ELZC features, the sixth consisting of 23 waveform, frequency, PE and ELZC features, and the last consisting of all 27 features. Up to 1500 mobile ECG recordings from the Physionet/Computing in Cardiology Challenge 2011 were employed in this study. Three indices i.e., sensitivity (Se), specificity (Sp) and accuracy (Acc), were used for evaluating performances of the classifiers on the seven feature schemes, respectively. The experiment results indicated PE and ELZC can help to improve performance of the aforementioned four classifiers for assessing ECG quality. Using all features except ApEn features obtained the best performances for each classifier. For this sixth scheme, the LS-SVM yielded the highest Acc of 92.20% on hidden test data, as well as a relatively high Acc of 93.60% on training data. Compared with the other classifiers, the LS-SVM classifier also demonstrated the superior generalization ability
Identification of Chimera using Machine Learning
Chimera state refers to coexistence of coherent and non-coherent phases in
identically coupled dynamical units found in various complex dynamical systems.
Identification of Chimera, on one hand is essential due to its applicability in
various areas including neuroscience, and on other hand is challenging due to
its widely varied appearance in different systems and the peculiar nature of
its profile. Therefore, a simple yet universal method for its identification
remains an open problem. Here, we present a very distinctive approach using
machine learning techniques to characterize different dynamical phases and
identify the chimera state from given spatial profiles generated using various
different models. The experimental results show that the performance of the
classification algorithms varies for different dynamical models. The machine
learning algorithms, namely random forest, oblique random forest based on
tikhonov, parallel-axis split and null space regularization achieved more than
accuracy for the Kuramoto model. For the logistic-maps, random forest
and tikhonov regularization based oblique random forest showed more than
accuracy, and for the H\'enon-Map model, random forest, null-space and
axis-parallel split regularization based oblique random forest achieved more
than accuracy. The oblique random forest with null space regularization
achieved consistent performance (more than accuracy) across different
dynamical models while the auto-encoder based random vector functional link
neural network showed relatively lower performance. This work provides a
direction for employing machine learning techniques to identify dynamical
patterns arising in coupled non-linear units on large-scale, and for
characterizing complex spatio-temporal patterns in real-world systems for
various applications.Comment: 20 Pages, 4 Figures; Comments welcom
Random Shapley Forests: Cooperative Game Based Random Forests with Consistency
The original random forests algorithm has been widely used and has achieved excellent performance for the classification and regression tasks. However, the research on the theory of random forests lags far behind its applications. In this paper, to narrow the gap between the applications and theory of random forests, we propose a new random forests algorithm, called random Shapley forests (RSFs), based on the Shapley value. The Shapley value is one of the well-known solutions in the cooperative game, which can fairly assess the power of each player in a game. In the construction of RSFs, RSFs uses the Shapley value to evaluate the importance of each feature at each tree node by computing the dependency among the possible feature coalitions. In particular, inspired by the existing consistency theory, we have proved the consistency of the proposed random forests algorithm. Moreover, to verify the effectiveness of the proposed algorithm, experiments on eight UCI benchmark datasets and four real-world datasets have been conducted. The results show that RSFs perform better than or at least comparable with the existing consistent random forests, the original random forests and a classic classifier, support vector machines
Water filtration by using apple and banana peels as activated carbon
Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent
A novel approach to data mining using simplified swarm optimization
Data mining has become an increasingly important approach to deal with the rapid
growth of data collected and stored in databases. In data mining, data classification
and feature selection are considered the two main factors that drive people when
making decisions. However, existing traditional data classification and feature
selection techniques used in data management are no longer enough for such massive
data. This deficiency has prompted the need for a new intelligent data mining
technique based on stochastic population-based optimization that could discover
useful information from data.
In this thesis, a novel Simplified Swarm Optimization (SSO) algorithm is proposed as
a rule-based classifier and for feature selection. SSO is a simplified Particle Swarm
Optimization (PSO) that has a self-organising ability to emerge in highly distributed
control problem space, and is flexible, robust and cost effective to solve complex
computing environments. The proposed SSO classifier has been implemented to
classify audio data. To the author’s knowledge, this is the first time that SSO and PSO
have been applied for audio classification.
Furthermore, two local search strategies, named Exchange Local Search (ELS) and
Weighted Local Search (WLS), have been proposed to improve SSO performance.
SSO-ELS has been implemented to classify the 13 benchmark datasets obtained from
the UCI repository database. Meanwhile, SSO-WLS has been implemented in
Anomaly-based Network Intrusion Detection System (A-NIDS). In A-NIDS, a novel
hybrid SSO-based Rough Set (SSORS) for feature selection has also been proposed.
The empirical analysis showed promising results with high classification accuracy
rate achieved by all proposed techniques over audio data, UCI data and KDDCup 99
datasets. Therefore, the proposed SSO rule-based classifier with local search
strategies has offered a new paradigm shift in solving complex problems in data
mining which may not be able to be solved by other benchmark classifiers