Instance-based Learning Model Research Articles

Modeling Knowledge Graphs with Composite Reasoning

The ability to combine multiple pieces of existing knowledge to infer new knowledge is both crucial and challenging. In this paper, we explore how facts of various entities are combined in the context of knowledge graph completion (KGC). We use composite reasoning to unify the views from different KGC models, including translational models, tensor factorization (TF)-based models, instance-based learning models, and KGC regularizers. Moreover, our comprehensive examination of composite reasoning revealed an unexpected phenomenon: certain TF-based models learn embeddings with erroneous composite reasoning, which ultimately violates their fundamental collaborative filtering assumption and reduces their effects. This motivates us to reduce their composition error. Empirical evaluations demonstrate that mitigating the composition risk not only enhances the performance of TF-based models across all tested settings, but also surpass or is competitive with the state-of-the-art performance on two out of four benchmarks.

Generative Environment-Representation Instance-Based Learning: A Cognitive Model

Instance-Based Learning Theory (IBLT) suggests that humans learn to engage in dynamic decision making tasks through the accumulation of experiences, represented by the decision task features, the actions performed, and the utility of decision outcomes. This theory has been applied to the design of Instance-Based Learning (IBL) models of human behavior in a variety of contexts. One key feature of all IBL model applications is the method of accumulating instance-based memory and performing recognition-based retrieval. In simple tasks with few features, this knowledge representation and retrieval could hypothetically be done using all relevant information. However, these methods do not scale well to complex tasks when exhaustive enumeration of features is unfeasible. This requires cognitive modelers to design task-specific representations of state features, as well as similarity metrics, which can be time consuming and fail to generalize to related tasks. To address this issue, we leverage recent advancements in Artificial Neural Networks, specifically generative models (GMs), to learn representations of complex dynamic decision making tasks without relying on domain knowledge. We evaluate a range of GMs in their usefulness in forming representations that can be used by IBL models to predict human behavior in a complex decision making task. This work connects generative and cognitive models by using GMs to form representations and determine similarity.

Nano scale instance-based learning using non-specific hybridization of DNA sequences

DNA, or deoxyribonucleic acid, is a powerful molecule that plays a fundamental role in storing and processing genetic information of all living organisms. In recent years, scientists have harnessed hybridization powers between DNA molecules to perform various computing tasks in DNA computing and DNA storage. Unlike specific hybridization, non-specific hybridization provides a natural way to measure similarity between the objects represented by different DNA sequences. We utilize such property to build an instance-based learning model which recognizes an object by its similarity with other samples. The handwriting digit images in MNIST dataset are encoded by DNA sequences using a deep learning encoder. And the reverse complement sequence of a query image is used to hybridize with the training instance sequences. Simulation results by NUPACK show that this classification model by DNA could achieve 95% accuracy on average. Wet-lab experiments also validate the predicted yield is consistent with the hybridization strength. Our work proves that it is feasible to build an effective instance-based classification model for practical application.

An instance-based-learning simulation model to predict knowledge assets evolution involved in potential digital transformation projects

ABSTRACT Software engineering professionals must consider the appropriate technological solutions to meet their client’s needs and the organisational impact. The decision to implement a solution is not explicitly based on how it empowers the knowledge assets. Organisational knowledge assets are the foundation of the knowledge economy and a key element in evaluating the health of an organisation. This paper provides software engineers with a simulation model which illustrates the decision-making process for the implementation of technological solutions based on an evaluation of their client’s knowledge assets and how such assets impact and are impacted by the deployment of a solution. We use an agent-based approach and implement an instance-based learning model (a cognitive approach) to represent scenarios for experience-based decisions. 11 case studies were used to train the prediction engine and validate the usefulness of the model in generating scenarios and nurturing decision-making and user experiences.

Machine Learning Modeling of Forest Road Construction Costs

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.

Towards a Cognitive Theory of Cyber Deception.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market

Peer-to-Peer (P2P) lending has grown rapidly in the past years. Therefore, borrowers and lenders are provided with the opportunity of lending and borrowing independently of the banks. Lenders in the P2P lending market can share their total investment amount among different loans, so making a decision may be difficult for inexpert lenders. The aim of this study is to propose a novel decision-making framework in which instance-based learning as a lazy learning method and artificial neural networks as an eager learning approach are integrated. The proposed hybrid instance-based learning (HIBL) model has the ability to predict the return and risk of new loans and help investors to find the optimal portfolio. In order to check the effectiveness of our model, we use a real-world dataset from one of the most popular P2P lending marketplaces, namely Lending Club. Moreover, a comparison among our proposed model and a rating-based method reveals that the proposed HIBL model can improve investments in P2P lending.

Learning About the Effects of Alert Uncertainty in Attack and Defend Decisions via Cognitive Modeling

We aim to learn about the cognitive mechanisms governing the decisions of attackers and defenders in cybersecurity involving intrusion detection systems (IDSs). Prior research has experimentally studied the role of the presence and accuracy of IDS alerts on attacker's and defender's decisions using a game-theoretic approach. However, little is known about the cognitive mechanisms that govern these decisions. To investigate the cognitive mechanisms governing the attacker's and defender's decisions in the presence of IDSs of different accuracies, instance-based learning (IBL) models were developed. One model (NIDS) disregarded the IDS alerts and one model (IDS) considered them in the instance structure. Both the IDS and NIDS models were trained in an existing dataset where IDSs were either absent or present and they possessed different accuracies. The calibrated IDS model was tested in a newly collected test dataset where IDSs were present 50% of the time and they possessed different accuracies. Both the IDS and NIDS models were able to account for human decisions in the training dataset, where IDS was absent or present and it possessed different accuracies. However, the IDS model could accurately predict the decision-making in only one of the several IDS accuracy conditions in the test dataset. Cognitive models like IBL may provide some insights regarding the cognitive mechanisms governing the decisions of attackers and defenders in conditions not involving IDSs or IDSs of different accuracies. IBL models may be helpful for penetration testing exercises in scenarios involving IDSs of different accuracies.

Semisupervised sentiment analysis method for online text reviews

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

Selective prototype-based learning on concept-drifting data streams

Data stream mining has gained increasing attention in recent years due to its wide range of applications. In this paper, we propose a new selective prototype-based learning (SPL) method on evolving data streams, which dynamically maintains representative instances to capture the time-changing concepts, and make predictions in a local fashion. As an instance-based learning model, SPL only maintains some important prototypes (i.e., ISet) via error-driven representativeness learning. The fast condensed nearest neighbor (FCNN) rule, is further introduced to compress these prototypes, making the algorithm also applicable under memory constraints. To better distinguish noises from the instances associated with the new emerging concept, a potential concept instance set (i.e., PSet) is used to store all misclassified instances. Relying on the potential concept instance set, a local-aware distribution-based concept drift detection approach is proposed. SPL has several attractive benefits: (a) it can fit the evolving data streams very well by maintaining a small size of instance set; (b) it is capable of capturing both gradual and sudden concept drifts effectively; (c) it has great capabilities to distinguish noise/outliers from drifting instances. Experimental results show that the SPL has better classification performance than many other state-of-the-art algorithms.

110th Anniversary: Real-Time End Point Detection of Fluidized Bed Drying Process Based on a Switching Model of Near-Infrared Spectroscopy

For timely detection of the drying end point of a fluidized bed drying (FBD) process, a switching model based monitoring method is proposed based on in situ measurement of granule moisture content via near-infrared (NIR) spectroscopy. The least-squares support vector classification (LSSVC) method is adopted to build a global model for monitoring the initial underdrying phase with relatively higher granule moisture content. Subsequently, the instance based learning (IBL) strategy is used to select similar samples from historical batches for building up a local model to check on each query sample in the current process, in order to detect whether the real drying end point is reached. To solve the problem of selecting similar samples in high-dimensional NIR spectral space, the t-distributed stochastic neighbor embedding (t-SNE) strategy is introduced into the IBL model building method to ensure efficiency of dimension reduction. For online monitoring of an FBD process, a model switch strategy is proposed bet...

The initial condition problem with complete history dependency in learning models for travel choices

Learning-based models that capture travelers’ day-to-day learning processes in repeated travel choices could benefit from ubiquitous sensors such as smartphones, which provide individual-level longitudinal data to help validate and improve such models. However, the common problem of missing initial observations in longitudinal data collection can lead to inconsistent estimates of perceived value of attributes in question, and thus inconsistent parameter estimates. In this paper, the stated problem is addressed by treating the missing observations as latent variables in an instance-based learning model that is estimated via maximum simulated likelihood (MSL). The MSL method is implemented in practice using random sampling and importance sampling. Monte Carlo experimentation based on synthetic data shows that both the MSL with random sampling (MSLrs) and MSL with importance sampling (MSLis) are effective in correcting for the endogeneity problem in that the percent error and empirical coverage of the estimators are greatly improved after the correction. Compared to the MSLrs method, the MSLis method is superior in both effectiveness and computational efficiency. Furthermore, MSLis passes a formal statistical test for the recovery of the population values up to a scale with a large number of missing observations, while MSLrs systematically fails due to the curse of dimensionality. The impacts of sampling size in MSLrs and number of high probability choice sequences in MSLis on the methods’ performances are investigated the methods are applied to an experimental route-choice dataset to demonstrate their empirical application. Hausman–McFadden tests show that the estimators after correction are statistically equal to the estimators of the full dataset without missing observations, confirming that the proposed methods are practical and effective for addressing the stated problem.

An exploratory study of instance-based learning for route choice with random travel times

Availability of individual-level longitudinal data provides the opportunity to better understand travelers' day-to-day learning behavior, enabling more accurate predictions of traffic patterns in a network with random travel times. In this paper, an instance-based learning (IBL) model that can capture the recency, hot stove and payoff variability effects embedded in travelers' day-to-day learning processes is developed for route-choice based on the power law of forgetting and practice. Experiments based on synthetic datasets show that the true parameter values of the IBL model can be consistently retrieved and the model can potentially predict different traffic patterns compared to non-learning models. The IBL model is compared with a baseline learning model using an experimental dataset of repeated route-choice. Estimation results show that the IBL model reveals higher sensitivity to perceived travel time and achieves better model fit. Cross validation experiments suggest that the forecasting ability of the IBL model is consistently better than the baseline learning model. Practical considerations for choice modeling are further discussed.

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.

The initial condition problem with complete history dependency in learning models for travel choices

Abstract: Learning-based models that capture travelers’ day-to-day learning processes in repeated travel choices could benefit from ubiquitous sensors such as smartphones, which provide individual-level longitudinal data to help validate and improve such models. However, the common problem of missing initial observations in longitudinal data collection can lead to inconsistent estimates of perceived value of attributes in question, and thus inconsistent parameter estimates. In this paper, the stated problem is addressed by treating the missing observations as latent variables. The proposed method is implemented in practice as maximum simulated likelihood (MSL) correction with two sampling methods in an instance-based learning model for travel choice, and the finite sample bias and efficiency of the estimators are investigated. Monte Carlo experimentation based on synthetic data shows that both the MSL with random sampling (MSLrs) and MSL with importance sampling (MSLis) are effective in correcting for the endogeneity problem in that the percent error and empirical coverage of the estimators are greatly improved after correction. Compared to the MSLrs method, the MSLis method is superior in both effectiveness and computational efficiency. Furthermore, MSLis passes a formal statistical test for the recovery of the population values up to a scale with a large number of missing observations, while MSLrs systematically fails due to the curse of dimensionality. The impacts of sampling size in MSLrs and number of high probability choice sequences in MSLis on the methods’ performances are investigated. The methods are applied to an experimental route-choice dataset to demonstrate their empirical application. Hausman-McFadden tests show that the estimators after correction are statistically equal to the estimators of the full dataset without missing observations, confirming that the proposed methods are practical and effective for addressing the stated problem.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

Accounting for outcome and process measures in dynamic decision-making tasks through model calibration

Computational models of learning and the theories they represent are often validated by calibrating them to human data on decision outcomes. However, only a few models explain the process by which these decision outcomes are reached. We argue that models of learning should be able to reflect the process through which the decision outcomes are reached, and validating a model on the process is likely to help simultaneously explain both the process as well as the decision outcome. To demonstrate the proposed validation, we use a large dataset from the Technion Prediction Tournament and an existing Instance-based Learning model. We present two ways of calibrating the model’s parameters to human data: on an outcome measure and on a process measure. In agreement with our expectations, we find that calibrating the model on the process measure helps to explain both the process and outcome measures compared to calibrating the model on the outcome measure. These results hold when the model is generalized to a different dataset. We discuss implications for explaining the process and the decision outcomes in computational models of learning. Supplementary files Supplementary files are available here .

Framing From Experience: Cognitive Processes and Predictions of Risky Choice.

A framing bias shows risk aversion in problems framed as "gains" and risk seeking in problems framed as "losses," even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias insituations where decision makers rely on their own experience, sampling the problem's options (safe and risky) and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed.

A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads

Due to the disastrous consequences of slope failures, forecasting their occurrences is a practical need of government agencies to develop strategic disaster prevention programs. This research proposes a Swarm-Optimized Fuzzy Instance-based Learning (SOFIL) model for predicting slope collapses. The proposed model utilizes the Fuzzy k-Nearest Neighbor (FKNN) algorithm as an instance-based learning method to predict slope collapse events. Meanwhile, to determine the model’s hyper-parameters appropriately, the Firefly Algorithm (FA) is employed as an optimization technique. Experimental results have pointed out that the newly established SOFIL can outperform other benchmarking algorithms. Therefore, the proposed model is very promising to help decision-makers in coping with the slope collapse prediction problem.

Refuting data aggregation arguments and how the instance-based learning model stands criticism: A reply to Hills and Hertwig (2012).

Refuting data aggregation arguments and how the instance-based learning model stands criticism: A reply to Hills and Hertwig (2012).