Probability Model Based on Cluster Analysis to Classify Sequences of Observations for Small Training Sets

Abstract

The problem of recognizing patterns, when there are few training data available, is particularly relevant and arises in cases when collection of training data is expensive or essentially impossible. The work proposes a new probability model MC&CL (Markov Chain and Clusters) based on a combination of markov chain and algorithm of clustering (self-organizing map of Kohonen, k-means method), to solve a problem of classifying sequences of observations, when the amount of training dataset is low. An original experimental comparison is made between the developed model (MC&CL) and a number of the other popular models to classify sequences: HMM (Hidden Markov Model), HCRF (Hidden Conditional Random Fields),LSTM (Long Short-Term Memory), kNN+DTW (k-Nearest Neighbors algorithm + Dynamic Time Warping algorithm). A comparison is made using synthetic random sequences, generated from the hidden markov model, with noise added to training specimens. The best accuracy of classifying the suggested model is shown, as compared to those under review, when the amount of training data is low.

Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data

“…Lines 14 and 15 generate an additional resolution by averaging two adjacent points in the two time series at the current lowest resolution. In lines [16][17][18][19], projection and refinement processes are repeated to calculate from the lowest resolution to the original resolution. The both lines 17 and 19 measure the similarity and find the optimal warping path.…”

“…Also, it cannot be applied to time series datasets with non-uniform lengths [16]. HMM has few parameters and is suitable for time series datasets with few training data [17]. But, HMM is complex and has poor performance [18], [19].…”

Data Flow Optimization in the Internet of Things

“…IoT oriented various platforms and applications that exploit communication among heterogeneous and nonheterogeneous devices to execute tasks and provide real-time services [7,8,9,10,11,12]. It creates a new type…”