Digital embryos – A novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Pigeons are classic model animals to study perceptual category learning. A theoretical understanding of the cognitive mechanisms of categorization requires a careful consideration of the employed stimulus material. Optimally, stimuli should not consist of real-world objects that might be associated with prior experience. The number of exemplars should be theoretically infinite and easy to produce. In addition, the experimenter should have the freedom to produce 2D- and 3D-versions of the stimuli and, finally, the stimulus set should provide the opportunity to identify the diagnostic elements that the animals use. To this end, we used the approach of "virtual phylogenesis" of "digital embryos" to produce two stimulus sets of objects that meet these criteria. In our experiment pigeons learned to categorize these stimuli in a forced-choice procedure. In addition, we used peck tracking to identify where on the stimulus the animals pecked to signal their choice. Pigeons learned the task and transferred successfully to novel exemplars. Using a k-nearest neighbor classifier, we were able to predict the presented stimulus class based on pecking location indicating that pecks are related to features of interest. We further identified potential strategies of the pigeons through this approach, namely that they were either learning one or two categories to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

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Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning

Animal Cognition ◽

10.1007/s10071-021-01594-1 ◽

2022 ◽

Author(s):

Roland Pusch ◽

Julian Packheiser ◽

Charlotte Koenen ◽

Fabrizio Iovine ◽

Onur Güntürkün

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Learning Strategy ◽

Columba Livia ◽

Careful Consideration ◽

Cognitive Mechanisms ◽

K Nearest Neighbor ◽

Choice Procedure ◽

Perceptual Category ◽

Perceptual Category Learning

AbstractPigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy.

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KNN Loss and Deep KNN

Fundamenta Informaticae ◽

10.3233/fi-2021-2068 ◽

2021 ◽

Vol 182 (2) ◽

pp. 95-110

Author(s):

Linh Le ◽

Ying Xie ◽

Vijay V. Raghavan

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Nearest Neighbor ◽

Learning Strategy ◽

Feature Space ◽

Kernel Functions ◽

Training Data ◽

K Nearest Neighbor ◽

Map Data

The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions, namely KNN Loss and Fuzzy KNN Loss, to quantify the quality of neighborhoods formed by KNN with respect to supervised learning, such that minimizing the loss function on the training data leads to maximizing KNN decision accuracy on the training data. We further present a deep learning strategy that is able to learn, by minimizing KNN loss, pairwise similarities of data that implicitly maps data to a feature space where the quality of KNN neighborhoods is optimized. Experimental results show that this deep learning strategy (denoted as Deep KNN) outperforms state-of-the-art supervised learning methods on multiple benchmark data sets.

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

2018 ◽

pp. 429-441

Author(s):

M. Jeyanthi ◽

C. Velayutham

Keyword(s):

Nearest Neighbor ◽

Technology Development ◽

Vital Role ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Data Set ◽

Eeg Data ◽

Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i1.1735 ◽

2020 ◽

Vol 17 (1) ◽

pp. 319-328

Author(s):

Ade Muchlis Maulana Anwar ◽

Prihastuti Harsani ◽

Aries Maesya

Keyword(s):

Nearest Neighbor ◽

Information Gain ◽

Birth Certificate ◽

Population Data ◽

Community Services ◽

Birth Certificates ◽

Similar Data ◽

K Nearest Neighbor ◽

Civil Registration ◽

The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) ◽

10.23919/mipro48935.2020.9245195 ◽

2020 ◽

Author(s):

A. Sagdic ◽

C. Tekinbas ◽

E. Arslan ◽

T. Kucukyilmaz

Keyword(s):

Recommendation System ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

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Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i9.102 ◽

2018 ◽

Vol 6 (9) ◽

pp. 152

Author(s):

S. Vijaya Rani ◽

G. N. K. Suresh Babu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Error Rate ◽

Learning Process ◽

Nearest Neighbor ◽

Detection System ◽

Support Vector ◽

K Nearest Neighbor ◽

Artificial Neural

The illegal hackers penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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