Online Quantification of the Concept Drift Using Incremental Learned Classifier and Non-automatic Clustering

2018 ◽  
pp. 1069-1074
Author(s):  
Mohammad Hamed Ardakani ◽  
Ahmed Shokry ◽  
Gerard Escudero ◽  
Moisès Graells ◽  
Antonio Espuña
Keyword(s):  
Concept Drift ◽  
Automatic Clustering

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

Concept drift detection and localization in process mining

2021 ◽  
Author(s):  
Rafael Gaspar de Sousa ◽  
Sarajane Marques Peres ◽  
Marcelo Fantinato ◽  
Hajo Alexander Reijers
Keyword(s):  
Process Mining ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Detection And Localization

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification

scholarly journals Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 380
Author(s):  
Emanuele Cavenaghi ◽  
Gabriele Sottocornola ◽  
Fabio Stella ◽  
Markus Zanker
Keyword(s):  
Real World ◽  
Concept Drift ◽  
Empirical Evaluation ◽  
Sliding Window ◽  
Discount Factor ◽  
Data Streaming ◽  
Sources Of Information ◽  
Sequential Decision ◽  
Time Step ◽  
Thompson Sampling

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action is kept stationary by the environment through time. Nevertheless, in many real-world applications this assumption does not hold and the agent has to face a non-stationary environment, that is, with a changing reward distribution. Thus, we present a new MAB algorithm, named f-Discounted-Sliding-Window Thompson Sampling (f-dsw TS), for non-stationary environments, that is, when the data streaming is affected by concept drift. The f-dsw TS algorithm is based on Thompson Sampling (TS) and exploits a discount factor on the reward history and an arm-related sliding window to contrast concept drift in non-stationary environments. We investigate how to combine these two sources of information, namely the discount factor and the sliding window, by means of an aggregation function f(.). In particular, we proposed a pessimistic (f=min), an optimistic (f=max), as well as an averaged (f=mean) version of the f-dsw TS algorithm. A rich set of numerical experiments is performed to evaluate the f-dsw TS algorithm compared to both stationary and non-stationary state-of-the-art TS baselines. We exploited synthetic environments (both randomly-generated and controlled) to test the MAB algorithms under different types of drift, that is, sudden/abrupt, incremental, gradual and increasing/decreasing drift. Furthermore, we adapt four real-world active learning tasks to our framework—a prediction task on crimes in the city of Baltimore, a classification task on insects species, a recommendation task on local web-news, and a time-series analysis on microbial organisms in the tropical air ecosystem. The f-dsw TS approach emerges as the best performing MAB algorithm. At least one of the versions of f-dsw TS performs better than the baselines in synthetic environments, proving the robustness of f-dsw TS under different concept drift types. Moreover, the pessimistic version (f=min) results as the most effective in all real-world tasks.

scholarly journals Energy-aware very fast decision tree

2021 ◽  
Author(s):  
Eva García-Martín ◽  
Niklas Lavesson ◽  
Håkan Grahn ◽  
Emiliano Casalicchio ◽  
Veselka Boeva
Keyword(s):  
Energy Consumption ◽  
Decision Tree ◽  
Concept Drift ◽  
Algorithm Design ◽  
Energy Aware ◽  
Battery Capacity ◽  
Very Fast Decision Tree ◽  
Public Datasets ◽  
Additional Constraints ◽  
Fast Decision

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

scholarly journals Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 349-371
Author(s):  
Hassan Mehmood ◽  
Panos Kostakos ◽  
Marta Cortes ◽  
Theodoros Anagnostopoulos ◽  
Susanna Pirttikangas ◽  
...  
Keyword(s):  
Real World ◽  
Data Streams ◽  
Smart City ◽  
Smart Cities ◽  
Concept Drift ◽  
Distributed Environment ◽  
Real World Data ◽  
Unique Challenge ◽  
World Data ◽  
Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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