Optimal Score Function Research Articles

Learning to rank anomalies: scalar performance criteria and maximization of rank statistics

AbstractThe ability to collect and store ever more massive data, unlabeled in many cases, has been accompanied by the need to process them efficiently in order to extract relevant information and possibly design solutions based on the latter. In various situations, the vast majority of the observations exhibit the same behavior, while a small proportion deviates from it. Detecting these outlier observations (or equivalently defined as anomalies) is now one of the major challenges for machine learning applications (e.g. fraud detection or predictive maintenance). We propose here a novel methodology for outlier/anomaly detection, by learning a scoring function defined on the feature space allowing for ranking the observations by degree of abnormality. The scoring function is built through maximization of an empirical performance criterion taking the form of a (two-sample) linear rank statistic. We show that bipartite ranking algorithms can thus be used to learn nearly optimal scoring function with provable theoretical guarantees. We illustrate our methodology with numerical experiments based on open access online code.

Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm

Abstract The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called “committor function” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.

Data-driven methods to estimate the committor function in conceptual ocean models

Abstract. In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing. Such transitions are rare events in general, and simulating long-enough trajectories in order to gather sufficient data to determine transition statistics would be too expensive. Conversely, rare events algorithms like TAMS (trajectory-adaptive multilevel sampling) encourage the transition while keeping track of the model statistics. However, this algorithm relies on a score function whose choice is crucial to ensure its efficiency. The optimal score function, called the committor function, is in practice very difficult to compute. In this paper, we compare different data-based methods (analog Markov chains, neural networks, reservoir computing, dynamical Galerkin approximation) to estimate the committor from trajectory data. We apply these methods on two models of the Atlantic Ocean circulation featuring very different dynamical behavior. We compare these methods in terms of two measures, evaluating how close the estimate is from the true committor and in terms of the computational time. We find that all methods are able to extract information from the data in order to provide a good estimate of the committor. Analog Markov Chains provide a very reliable estimate of the true committor in simple models but prove not so robust when applied to systems with a more complex phase space. Neural network methods clearly stand out by their relatively low testing time, and their training time scales more favorably with the complexity of the model than the other methods. In particular, feedforward neural networks consistently achieve the best performance when trained with enough data, making this method promising for committor estimation in sophisticated climate models.

Towards Decision-Friendly AUC: Learning Multi-Classifier with AUCµ

Area Under the ROC Curve (AUC) is a widely used ranking metric in imbalanced learning due to its insensitivity to label distributions. As a well-known multiclass extension of AUC, Multiclass AUC (MAUC, a.k.a. M-metric) measures the average AUC of multiple binary classifiers. In this paper, we argue that simply optimizing MAUC is far from enough for imbalanced multi-classification. More precisely, MAUC only focuses on learning scoring functions via ranking optimization, while leaving the decision process unconsidered. Therefore, scoring functions being able to make good decisions might suffer from low performance in terms of MAUC. To overcome this issue, we turn to explore AUCµ, another multiclass variant of AUC, which further takes the decision process into consideration. Motivated by this fact, we propose a surrogate risk optimization framework to improve model performance from the perspective of AUCµ. Practically, we propose a two-stage training framework for multi-classification, where at the first stage a scoring function is learned maximizing AUCµ, and at the second stage we seek for a decision function to improve the F1-metric via our proposed soft F1. Theoretically, we first provide sufficient conditions that optimizing the surrogate losses could lead to the Bayes optimal scoring function. Afterward, we show that the proposed surrogate risk enjoys a generalization bound in order of O(1/√N). Experimental results on four benchmark datasets demonstrate the effectiveness of our proposed method in both AUCµ and F1-metric.

Paced-curriculum distillation with prediction and label uncertainty for image segmentation.

In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self-distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (P-CD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robot-assisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation.

Learning With Multiclass AUC: Theory and Algorithms.

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(√{1/N}) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework. The code is now available at https://github.com/joshuaas/Learning-with-Multiclass-AUC-Theory-and-Algorithms.

Optimality of regularized least squares ranking with imperfect kernels

Ranking is one of the central machine learning tasks arising from supervised learning and has many important applications in information retrieval. Many ranking approaches have been designed and extensively studied. In this paper we study two kernel ranking algorithms, the regularized least square ranking and its bias corrected version, which learn scoring functions from reproducing kernel Hilbert spaces (RKHSs). We say a kernel is perfect for ranking if an optimal scoring function lies in the associated RKHS. It is imperfect if an optimal scoring function can be approximated by the RKHS. The two regularized kernel ranking algorithms have been shown theoretically justified and empirically effective when the kernel is perfectly chosen. In practice, however, kernels are usually selected via cross validation process. It is more common than not that the kernel is imperfectly chosen. We develop a novel leave-two-out analysis technique to evaluate the generalization performance of the two regularized ranking algorithms in this situation. We show that they are still effective and can achieve the capacity independent optimal convergence rates.

Optimizing Partial Area Under the Top-k Curve: Theory and Practice.

Top- k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top- k optimization generally focuses on the optimization method of the top- k objective, while ignoring the limitations of the metric itself. In this paper, we point out that the top- k objective lacks enough discrimination such that the induced predictions may give a totally irrelevant label a top rank. To fix this issue, we develop a novel metric named partial Area Under the top- k Curve (AUTKC). Theoretical analysis shows that AUTKC has a better discrimination ability, and its Bayes optimal score function could give a correct top- K ranking with respect to the conditional probability. This shows that AUTKC does not allow irrelevant labels to appear in the top list. Furthermore, we present an empirical surrogate risk minimization framework to optimize the proposed metric. Theoretically, we present (1) a sufficient condition for Fisher consistency of the Bayes optimal score function; (2) a generalization upper bound which is insensitive to the number of classes under a simple hyperparameter setting. Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.

Iterative Multi-Attribute Procurement Auction with Decision Support for Bid Formulation

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.

Development of New Robust Optimal Score Function for the Weibull Distributed Error Term in Multilevel Models

A popular robust estimation technique for linear models is the rank-based method as an alternative to the ordinary least square (OLS) and restricted maximum likelihood (REML) in the presence of extreme observations. This method is applied in machine reliability analysis and quantum engineering, especially in artificial intelligence and optimization problems where outliers are commonly observed. This technique is also extended for the multilevel model, where the shape of error distribution contributes a significant role in more efficient estimation. In this study, we proposed the Weibull score function for the Weibull distributed error terms in the multilevel model. The efficiency of the proposed score function is compared with the existing Wilcoxon score function and the traditional method REML via Monte Carlo simulations after adding simulated extreme observations. For small values of shape parameter in Weibull distribution of error term showing the presence of outliers, the Weibull score function was found to be efficient as compared to the Wilcoxon and REML methods. However, for a large value of shape parameter, Wilcoxon score appeared either equally efficient than the Weibull score function. REML is observed least precise in all situations. These findings are verified through a real application on test scores data, with a small value of shape parameter, and the Weibull score function turned out the most efficient.

Soil quality response to organic amendments on dryland red soil in subtropical China

Organic fertilizer applications can increase soil fertility and productivity and reduce chemical fertilizer use. However, few experimental studies have focused on effects of organic fertilizers on soil quality in a field ecosystem. The overarching goal of this study was to develop a soil quality index (SQI) for fertilization management in red soil farmland in China. The specific objectives were to determine 1) appropriate soil quality indicators, 2) a suitable weight assignment method (PCA, principal component analysis or MRA, multiple regression analysis) and 3) the optimal scoring function (standard or linear or non-linear). We analyzed 40 soil physical, chemical, biochemical and biological properties under nine fertilization treatments (CK (no fertilizer), CF (chemical fertilizer), RF (60% chemical fertilizer), CFS (chemical fertilizer + straw), RFS (60% chemical fertilizer + straw), CFB (chemical fertilizer + biochar), RFB (60% chemical fertilizer + biochar), RFP (60% chemical fertilizer + pig manure), RFV (60% chemical fertilizer + vermicompost)) as potential soil quality indicators. Principal component analysis and correlation analysis were applied to determine the indicators responsive to management and for the confirmation of the six SQIs. Available phosphorous, nitrogen, potassium and gram-positive bacteria were chosen as the minimum data set from the total of 40 soil indictors. Management of organic and inorganic fertilizers affected each soil property in different ways, and the index based on the minimum data set, calculated using standard scoring function and weight assignment method of multiple regression analysis, was most accurate and sensitive. Application of a pig manure or vermicompost significantly increased SQI values relative to other treatments, followed by straw or biochar treatments which were higher than a single application of chemical fertilizer. These results indicate that the addition of organic fertilizers will not only improve soil quality, but also enhance rapeseed and sweet potato yield compared to chemical fertilizers. Additionally, the SQI method can provide a practical and quantitative tool for the evaluation of soil quality.

FASPR: an open-source tool for fast and accurate protein side-chain packing.

Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary data are available at Bioinformatics online.

Do changes in feature selection parameters influence the classification of knee rehabilitation exercises when using body worn accelerometer data?

Purpose: Exercise prescription plays a fundamental role in the treatment of knee pathologies such as knee osteoarthritis, where over 90,000 total knee replacement patients receive regular post-surgery physiotherapy each year in the UK. Physiotherapists rely on home-based exercise prescription yet have limited knowledge of patient engagement at home and find it difficult to objectively monitor patient progress, attribute functional improvement (or lack of) to adherence/non-adherence and prescribe personalised interventions. The research vision is to facilitate unobtrusive sensor driven home monitoring/feedback of knee rehabilitation exercises. As a first step, this study sought to fine tune a machine learning algorithm to classify between different knee exercises and understand the impact of different feature selection parameters on classification performance. Methods: 8 volunteers (4 healthy, 4 with self-reported history of knee pain/pathology but not receiving treatment) performed 15 repetitions of 4 knee rehabilitation exercises (sit to stand (STS), knee flexion (KFL), knee extension (KEX) and weight shifting (WSH)) whilst wearing lower limb Xsens inertial sensors sampling at 60 Hz. A total of 85 features were extracted per exercise repetition from tri-axial accelerometer data provided by sensors placed on the foot, shank, thigh and pelvis. These were defined as the 90th percentile spectral edge frequency ((SEF) in X,Y,Z axis) and signal mean, max min, variance, skewness, and kurtosis (all in the X,Y,Z axis) per sensor in addition to repetition length. Participants were split into training and testing datasets using a Leave-One-Group-Out cross validation (cv) where a participant with multiple repetitions represented a group. Within each of the 8 cv-folds, features were scaled, a univariate feature selection method was implemented to reduce the feature set to top ranking features and a linear support vector machine (SVM) classifier was performed to determine how well 4 different knee rehabilitation exercises could be distinguished from each other. This process was repeated using 3 different score function parameters (Mutual Information Classification (MIC), F-value Classification (F-Class) and Chi2) with the number of features being selected ranging from 1 - 10 to determine the optimal score function and number of features whilst optimising classification performance. The F1 score (weighted harmonic mean of precision and recall with a best score of 1) was computed within each cv-fold as an average F1 score across each of the exercises classes and then combined across cv-folds as a median and 1st - 3rd interquartile range. Results: Feature selection methods using the MIC and the F-Class score functions consistently outperformed that using the Chi2 (Figure 1). The MIC approach was chosen as the optimal feature selection method as the F-Class algorithm was marginally outperformed using fewer features with the optimal number of features (ie. fewest whilst retaining classifier performance) being 3. All 8 cv-folds using this method selected Z-axis acceleration 90th percentile SEF of the foot and shank sensors whilst the third feature varied slightly between cv-folds (mean Z-axis acceleration of the foot sensor (n=3/8 cv-folds); Y-axis mean acceleration of the thigh sensor (n=2/8 cv-folds), Z-axis acceleration variance of the foot (n=1/8 cv-folds) or shank (n=1/8 cv-folds) sensors and Z-axis mean acceleration of the shank sensor (n=1/8 cv-folds). Inspection of the individual F1-scores for each exercise using the optimal feature selection method revealed a reduced ability to classify KEX and KFL in comparison to STS and WSH exercises (Figure 2). Conclusions: Changing parameter settings within a univariate feature selection algorithm that identifies top ranking features was found to alter the performance of a linear SVM seeking to classify between 4 knee rehabilitation exercises. This finding confirms the impact that incorrect feature selection parameter settings may have on classification performance. From the parameters and feature selection methods considered, the combination of an MIC score function and the selection of the top 3 features appear optimal for this dataset. Whilst the 3 features selected appeared to consistently classify STS and WSH exercises well, this was not the case for KEX and KFL exercises with both varying considerably between the cv-folds and warrants further investigation. Future work will consider additional features that may improve the ability to discriminate between KEX and KFL exercises. Additional feature selection methods such as recursive feature elimination and removing features with low variance will be investigated to see whether SVM classification performance can be further enhanced.

Two-Stage Multi-attribute Auction Mechanism for Price Discovery and Winner Determination

This paper proposes a two-stage multi-attribute auction mechanism for market price discovery and to determine the winning bidder. In the first stage, the buyer announces a multi-attribute value function and a set of budget levels. The bidders submit sealed-bids for quality attributes against each budget level announced. The buyer uses the proposed procedure to estimate the cost function of each vendor and to determine the reservation quality for prequalification. Using these estimated cost functions, the buyer derives the optimal scoring function. In the second stage, the buyer announces the scoring function and conducts an open bid ascending auction using the qualified set of vendors from the first stage to determine the winner. The main purpose of the mechanism is to provide detailed information to the buyer about the procurement environment. This helps the buyer to make informed decisions while designing an appropriate scoring function. We showed that the proposed mechanism induces competitive bids from the vendors and there is no incentive for them to deviate from quality values submitted in the first stage.

Learning rate of gradient descent multi-dividing ontology algorithm

As acknowledge representation model, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is a key issue in these applications. One approach for ontology application is to learn an optimal ontology score function which maps each vertex in graph into a real-value. And the similarity between vertices is measured by the difference of their corresponding scores. The multi-dividing ontology algorithm is an ontology learning trick such that the model divides ontology vertices into k parts correspond to the k classes of rates. In this paper, we propose the gradient descent multi-dividing ontology algorithm based on iterative gradient computation and yield the learning rates with general convex losses by virtue of the suitable step size and regularisation parameter selection.

SP-Dock: Protein-Protein Docking Using Shape and Physicochemical Complementarity

In this paper, a framework for protein-protein docking is proposed, which exploits both shape and physicochemical complementarity to generate improved docking predictions. Shape complementarity is achieved by matching local surface patches. However, unlike existing approaches, which are based on single-patch or two-patch matching, we developed a new algorithm that compares simultaneously, groups of neighboring patches from the receptor with groups of neighboring patches from the ligand. Taking into account the fact that shape complementarity in protein surfaces is mostly approximate rather than exact, the proposed group-based matching algorithm fits perfectly to the nature of protein surfaces. This is demonstrated by the high performance that our method achieves especially in the case where the unbound structures of the proteins are considered. Additionally, several physicochemical factors, such as desolvation energy, electrostatic complementarity (EC), hydrophobicity (HP), Coulomb potential (CP), and Lennard-Jones potential are integrated using an optimized scoring function, improving geometric ranking in more than 60 percent of the complexes of Docking Benchmark 2.4.

Multicoil2: Predicting Coiled Coils and Their Oligomerization States from Sequence in the Twilight Zone

The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu.

Binding site prediction and improved scoring during flexible protein–protein docking with ATTRACT

The ATTRACT protein-protein docking program combined with a coarse-grained protein model has been used to predict protein-protein complex structures in CAPRI rounds 13-19. For six targets acceptable or better quality solutions have been submitted (high quality predictions for targets 32, 40, 41, and 42). The improved performance compared to previous rounds can be attributed in part to the inclusion of conformational flexibility during systematic searches and an optimized scoring function. In addition, a recently developed method for the prediction of putative protein binding sites based on the electrostatic penalty to place neutral low dielectric probes on the protein surface was applied to the most recent targets. The approach resulted in useful predictions of putative binding sites that can help to limit the systematic docking searches. Possible improvements of the docking approach in particular at the scoring and refinement steps are discussed.

Practical tests for randomized complete block designs

A class of affine-invariant test statistics, including a sign test and a related family of signed-rank tests, is proposed for randomized complete block designs with one observation per treatment. This class is obtained by using the transformation–retransformation approach of Chakraborty, Chaudhuri and Oja along with a directional transformation due to Tyler. Under the minimal assumption of directional symmetry of the underlying distribution, the null asymptotic distribution of the sign test statistic is shown to be chi-square with p - 1 degrees of freedom. The same null distribution is also proved for the family of signed-rank statistics under the assumption of symmetry of the underlying distribution. The Pitman asymptotic relative efficiencies of the tests, relative to Hotelling–Hsu's T 2 are established. Several score functions are discussed including a simple linear score function and the optimal normal score function. The test based on the linear score function is compared to the other members of this family and other statistics in the literature through efficiency calculations and Monte Carlo simulations. This statistic has an excellent performance over a wide range of distributions and for small as well as large dimensions.

Cooperative Metaheuristics for Exploring Proteomic Data

Most combinatorial optimization problems cannot be solved exactly. A class of methods, called metaheuristics, has proved its efficiency to give good approximated solutions in a reasonable time. Cooperative metaheuristics are a sub-set of metaheuristics, which implies a parallel exploration of the search space by several entities with information exchange between them. The importance of information exchange in the optimization process is related to the building block hypothesis of evolutionary algorithms, which is based on these two questions: what is the pertinent information of a given potential solution and how this information can be shared? A classification of cooperative metaheuristics methods depending on the nature of cooperation involved is presented and the specific properties of each class, as well as a way to combine them, is discussed. Several improvements in the field of metaheuristics are also given. In particular, a method to regulate the use of classical genetic operators and to define new more pertinent ones is proposed, taking advantage of a building block structured representation of the explored space. A hierarchical approach resting on multiple levels of cooperative metaheuristics is finally presented, leading to the definition of a complete concerted cooperation strategy. Some applications of these concepts to difficult proteomics problems, including automatic protein identification, biological motif inference and multiple sequence alignment are presented. For each application, an innovative method based on the cooperation concept is given and compared with classical approaches. In the protein identification problem, a first level of cooperation using swarm intelligence is applied to the comparison of mass spectrometric data with biological sequence database, followed by a genetic programming method to discover an optimal scoring function. The multiple sequence alignment problem is decomposed in three steps involving several evolutionary processes to infer different kind of biological motifs and a concerted cooperation strategy to build the sequence alignment according to their motif content.