A novel associative model for time series data mining

Parkinson's Disease Detection from Voice Recordings Using Associative Memories

Parkinson's disease (PD) is a neurological condition that is chronic and worsens over time, which presents a challenging diagnosis. An accurate diagnosis is required to recognize PD patients from healthy individuals. Diagnosing PD at early stages can reduce the severity of this disorder and improve the patient's living conditions. Algorithms based on associative memory (AM) have been applied in PD diagnosis using voice samples of patients with this health condition. Even though AM models have achieved competitive results in PD classification, they do not have any embedded component in the AM model that can identify and remove irrelevant features, which would consequently improve the classification performance. In this paper, we present an improvement to the smallest normalized difference associative memory (SNDAM) algorithm by means of a learning reinforcement phase that improves classification performance of SNDAM when it is applied to PD diagnosis. For the experimental phase, two datasets that have been widely applied for PD diagnosis were used. Both datasets were gathered from voice samples from healthy people and from patients who suffer from this condition at an early stage of PD. These datasets are publicly accessible in the UCI Machine Learning Repository. The efficiency of the ISNDAM model was contrasted with that of seventy other models implemented in the WEKA workbench and was compared to the performance of previous studies. A statistical significance analysis was performed to verify that the performance differences between the compared models were statistically significant. The experimental findings allow us to affirm that the proposed improvement in the SNDAM algorithm, called ISNDAM, effectively increases the classification performance compared against well-known algorithms. ISNDAM achieves a classification accuracy of 99.48%, followed by ANN Levenberg-Marquardt with 95.89% and SVM RBF kernel with 88.21%, using Dataset 1. ISNDAM achieves a classification accuracy of 99.66%, followed by SVM IMF1 with 96.54% and RF IMF1 with 94.89%, using Dataset 2. The experimental findings show that ISNDAM achieves competitive performance on both datasets and that statistical significance tests confirm that ISNDAM delivers classification performance equivalent to that of models published in previous studies.

Correlation Assessment of the Performance of Associative Classifiers on Credit Datasets Based on Data Complexity Measures

One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of the associative classifiers derived from Alpha-Beta models applied to the financial field. In this paper, the behavior of four associative classifiers was studied: the One-Hot version of the Hybrid Associative Classifier with Translation (CHAT-OHM), the Extended Gamma (EG), the Naïve Associative Classifier (NAC), and the Assisted Classification for Imbalanced Datasets (ACID). To establish the performance, we used the area under the curve (AUC), F-score, and geometric mean measures. The four classifiers were applied over 11 datasets from the financial area. Then, the performance of each one was analyzed, considering their correlation with the measures of data complexity, corresponding to six categories based on specific aspects of the datasets: feature, linearity, neighborhood, network, dimensionality, and class imbalance. The correlations that arise between the measures of complexity of the datasets and the measures of performance of the associative classifiers are established; these results are expressed with Spearman’s Rho coefficient. The experimental results correctly indicated correlations between data complexity measures and the performance of the associative classifiers.

An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer

A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed.

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.